SberBank Russian Housing Market*¶

#importing packages
import pandas as pd # data preprocessing
import numpy as np # linear algebra
import matplotlib.pyplot as plt #Basic plots visualisations
%matplotlib inline
import seaborn as sns #for prettier plots
import os
import pickle
import warnings
warnings.filterwarnings('ignore')
plt.style.use('fivethirtyeight')
plt.figure(figsize=(8,6))

pd.set_option('display.max_columns' , 300)
pd.set_option('display.max_rows' , 300)

<Figure size 576x432 with 0 Axes>

from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor , AdaBoostRegressor , GradientBoostingRegressor
from sklearn.svm import SVR
from sklearn.metrics import mean_absolute_error,mean_squared_error 
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from xgboost.sklearn import XGBRegressor

import os
os.getcwd()

'C:\\Users\\Hp\\Desktop\\Projects\\task\\russian housing market'

train_data=pd.read_csv("train_data.csv")

train_data.shape

(30471, 292)

def summary(train_data):
    print('Shape of data :' ,train_data.shape )
    return(pd.DataFrame({"Datatype":train_data.dtypes ,
                          "NAs":train_data.isnull().sum() , 
                           "Uniques":train_data.nunique(),
                           "levels": [train_data[x].unique() for x in train_data.columns]}))

summary(train_data)

Shape of data : (30471, 292)

Data Pre-Processing¶

* In data Pre-Processing we will not change the data just see what the data is all about,¶

*how many Numerical, Categorical Variables are Present¶

* Percentage of Missing values and plotting the median values for NA values and given records of that column¶

* Plotting the numerical and Categorical columns w.r.t Target Variable¶

1. Seeing the Percentage Of Missing Values¶

features_with_na=[features for features in train_data.columns if train_data[features].isnull().sum()>1]

for feature in features_with_na:
    print(feature, np.round(train_data[feature].isnull().mean(), 4),  ' % missing values')

life_sq 0.2095  % missing values
floor 0.0055  % missing values
max_floor 0.3141  % missing values
material 0.3141  % missing values
build_year 0.4465  % missing values
num_room 0.3141  % missing values
kitch_sq 0.3141  % missing values
state 0.445  % missing values
preschool_quota 0.2195  % missing values
school_quota 0.2194  % missing values
hospital_beds_raion 0.4739  % missing values
raion_build_count_with_material_info 0.1638  % missing values
build_count_block 0.1638  % missing values
build_count_wood 0.1638  % missing values
build_count_frame 0.1638  % missing values
build_count_brick 0.1638  % missing values
build_count_monolith 0.1638  % missing values
build_count_panel 0.1638  % missing values
build_count_foam 0.1638  % missing values
build_count_slag 0.1638  % missing values
build_count_mix 0.1638  % missing values
raion_build_count_with_builddate_info 0.1638  % missing values
build_count_before_1920 0.1638  % missing values
build_count_1921-1945 0.1638  % missing values
build_count_1946-1970 0.1638  % missing values
build_count_1971-1995 0.1638  % missing values
build_count_after_1995 0.1638  % missing values
metro_min_walk 0.0008  % missing values
metro_km_walk 0.0008  % missing values
railroad_station_walk_km 0.0008  % missing values
railroad_station_walk_min 0.0008  % missing values
ID_railroad_station_walk 0.0008  % missing values
cafe_sum_500_min_price_avg 0.4359  % missing values
cafe_sum_500_max_price_avg 0.4359  % missing values
cafe_avg_price_500 0.4359  % missing values
cafe_sum_1000_min_price_avg 0.2141  % missing values
cafe_sum_1000_max_price_avg 0.2141  % missing values
cafe_avg_price_1000 0.2141  % missing values
cafe_sum_1500_min_price_avg 0.1378  % missing values
cafe_sum_1500_max_price_avg 0.1378  % missing values
cafe_avg_price_1500 0.1378  % missing values
cafe_sum_2000_min_price_avg 0.0566  % missing values
cafe_sum_2000_max_price_avg 0.0566  % missing values
cafe_avg_price_2000 0.0566  % missing values
cafe_sum_3000_min_price_avg 0.0325  % missing values
cafe_sum_3000_max_price_avg 0.0325  % missing values
cafe_avg_price_3000 0.0325  % missing values
prom_part_5000 0.0058  % missing values
cafe_sum_5000_min_price_avg 0.0097  % missing values
cafe_sum_5000_max_price_avg 0.0097  % missing values
cafe_avg_price_5000 0.0097  % missing values

2. Plotting w.r.t Columns Having NA Values VS Present Records¶

for feature in features_with_na:
    data = train_data.copy()

    data[feature] = np.where(data[feature].isnull(), 1, 0)
    
    
    data.groupby(feature)['price_doc'].median().plot.bar()
    plt.title(feature)
    plt.show()

3.NUMERICAL VARIABLES¶

* We are having different types of numerical variables¶

a.Numerical Continous Variables¶

b.Numerical Discrete Variables¶

c.Date variables¶

numerical_features = [feature for feature in train_data.columns if train_data[feature].dtypes != 'O']

print('Number of numerical variables: ', len(numerical_features))


train_data[numerical_features].head()

Number of numerical variables:  276

C. Date variables Present or not¶

year_feature = [feature for feature in numerical_features if 'Yr' in feature or 'Year' in feature or "date" in feature]

year_feature

['raion_build_count_with_builddate_info']

train_data["raion_build_count_with_builddate_info"]

0        211.0
1        244.0
2        330.0
3        459.0
4        746.0
         ...  
30466    282.0
30467    650.0
30468      NaN
30469    186.0
30470    303.0
Name: raion_build_count_with_builddate_info, Length: 30471, dtype: float64

B. Numerical Discrete Variables¶

discrete_feature=[feature for feature in numerical_features if len(train_data[feature].unique())<25 and feature not in year_feature+['id']]
print("Discrete Variables Count: {}".format(len(discrete_feature)))

Discrete Variables Count: 46

for feature in discrete_feature:
    data=train_data.copy()
    data.groupby(feature)['price_doc'].median().plot.bar()
    plt.xlabel(feature)
    plt.ylabel('price_doc')
    plt.title(feature)
    plt.show()

A. Numerical Continous Variables¶

continuous_feature=[feature for feature in numerical_features if feature not in discrete_feature+year_feature+['id']]
print("Continuous feature Count {}".format(len(continuous_feature)))

Continuous feature Count 228

for feature in continuous_feature:
    data=train_data.copy()
    data[feature].hist(bins=25)
    plt.xlabel(feature)
    plt.ylabel("Count")
    plt.title(feature)
    plt.show()

Applying Log¶

for feature in continuous_feature:
    data=train_data.copy()
    if 0 in data[feature].unique():
        pass
    else:
        data[feature]=np.log(data[feature])
        data['price_doc']=np.log(data['price_doc'])
        plt.scatter(data[feature],data['price_doc'])
        plt.xlabel(feature)
        plt.ylabel('price_doc')
        plt.title(feature)
        plt.show()

Finding Outliers¶

for feature in continuous_feature:
    data=train_data.copy()
    if 0 in data[feature].unique():
        pass
    else:
        data[feature]=np.log(data[feature])
        data.boxplot(column=feature)
        plt.ylabel(feature)
        plt.title(feature)
        plt.show()

CATEGORICAL VARIABLES¶

categorical_features=[feature for feature in train_data.columns if train_data[feature].dtypes=='O']
len(categorical_features)

16

for feature in categorical_features:
    print('The feature is {} and number of categories are {}'.format(feature,len(train_data[feature].unique())))

The feature is timestamp and number of categories are 1161
The feature is product_type and number of categories are 2
The feature is sub_area and number of categories are 146
The feature is culture_objects_top_25 and number of categories are 2
The feature is thermal_power_plant_raion and number of categories are 2
The feature is incineration_raion and number of categories are 2
The feature is oil_chemistry_raion and number of categories are 2
The feature is radiation_raion and number of categories are 2
The feature is railroad_terminal_raion and number of categories are 2
The feature is big_market_raion and number of categories are 2
The feature is nuclear_reactor_raion and number of categories are 2
The feature is detention_facility_raion and number of categories are 2
The feature is water_1line and number of categories are 2
The feature is big_road1_1line and number of categories are 2
The feature is railroad_1line and number of categories are 2
The feature is ecology and number of categories are 5

for feature in categorical_features:
    data=train_data.copy()
    data.groupby(feature)['price_doc'].median().plot.bar()
    plt.xlabel(feature)
    plt.ylabel('price_doc')
    plt.title(feature)
    plt.show()

FEATURE ENGINEERING¶

train_data["timestamp"]

0        2011-08-20
1        2011-08-23
2        2011-08-27
3        2011-09-01
4        2011-09-05
            ...    
30466    2015-06-30
30467    2015-06-30
30468    2015-06-30
30469    2015-06-30
30470    2015-06-30
Name: timestamp, Length: 30471, dtype: object

train_data['date']=pd.to_datetime(train_data.timestamp)

train_data.drop("timestamp",axis=1,inplace=True)

train_data['year']=train_data.date.dt.year

train_data['month']=train_data.date.dt.month

train_data['day']=train_data.date.dt.day

train_data.drop("date",axis=1,inplace=True)

train_data.dtypes

id                                         int64
full_sq                                    int64
life_sq                                  float64
floor                                    float64
max_floor                                float64
material                                 float64
build_year                               float64
num_room                                 float64
kitch_sq                                 float64
state                                    float64
product_type                              object
sub_area                                  object
area_m                                   float64
raion_popul                                int64
green_zone_part                          float64
indust_part                              float64
children_preschool                         int64
preschool_quota                          float64
preschool_education_centers_raion          int64
children_school                            int64
school_quota                             float64
school_education_centers_raion             int64
school_education_centers_top_20_raion      int64
hospital_beds_raion                      float64
healthcare_centers_raion                   int64
university_top_20_raion                    int64
sport_objects_raion                        int64
additional_education_raion                 int64
culture_objects_top_25                    object
culture_objects_top_25_raion               int64
shopping_centers_raion                     int64
office_raion                               int64
thermal_power_plant_raion                 object
incineration_raion                        object
oil_chemistry_raion                       object
radiation_raion                           object
railroad_terminal_raion                   object
big_market_raion                          object
nuclear_reactor_raion                     object
detention_facility_raion                  object
full_all                                   int64
male_f                                     int64
female_f                                   int64
young_all                                  int64
young_male                                 int64
young_female                               int64
work_all                                   int64
work_male                                  int64
work_female                                int64
ekder_all                                  int64
ekder_male                                 int64
ekder_female                               int64
0_6_all                                    int64
0_6_male                                   int64
0_6_female                                 int64
7_14_all                                   int64
7_14_male                                  int64
7_14_female                                int64
0_17_all                                   int64
0_17_male                                  int64
0_17_female                                int64
16_29_all                                  int64
16_29_male                                 int64
16_29_female                               int64
0_13_all                                   int64
0_13_male                                  int64
0_13_female                                int64
raion_build_count_with_material_info     float64
build_count_block                        float64
build_count_wood                         float64
build_count_frame                        float64
build_count_brick                        float64
build_count_monolith                     float64
build_count_panel                        float64
build_count_foam                         float64
build_count_slag                         float64
build_count_mix                          float64
raion_build_count_with_builddate_info    float64
build_count_before_1920                  float64
build_count_1921-1945                    float64
build_count_1946-1970                    float64
build_count_1971-1995                    float64
build_count_after_1995                   float64
ID_metro                                   int64
metro_min_avto                           float64
metro_km_avto                            float64
metro_min_walk                           float64
metro_km_walk                            float64
kindergarten_km                          float64
school_km                                float64
park_km                                  float64
green_zone_km                            float64
industrial_km                            float64
water_treatment_km                       float64
cemetery_km                              float64
incineration_km                          float64
railroad_station_walk_km                 float64
railroad_station_walk_min                float64
ID_railroad_station_walk                 float64
railroad_station_avto_km                 float64
railroad_station_avto_min                float64
ID_railroad_station_avto                   int64
public_transport_station_km              float64
public_transport_station_min_walk        float64
water_km                                 float64
water_1line                               object
mkad_km                                  float64
ttk_km                                   float64
sadovoe_km                               float64
bulvar_ring_km                           float64
kremlin_km                               float64
big_road1_km                             float64
ID_big_road1                               int64
big_road1_1line                           object
big_road2_km                             float64
ID_big_road2                               int64
railroad_km                              float64
railroad_1line                            object
zd_vokzaly_avto_km                       float64
ID_railroad_terminal                       int64
bus_terminal_avto_km                     float64
ID_bus_terminal                            int64
oil_chemistry_km                         float64
nuclear_reactor_km                       float64
radiation_km                             float64
power_transmission_line_km               float64
thermal_power_plant_km                   float64
ts_km                                    float64
big_market_km                            float64
market_shop_km                           float64
fitness_km                               float64
swim_pool_km                             float64
ice_rink_km                              float64
stadium_km                               float64
basketball_km                            float64
hospice_morgue_km                        float64
detention_facility_km                    float64
public_healthcare_km                     float64
university_km                            float64
workplaces_km                            float64
shopping_centers_km                      float64
office_km                                float64
additional_education_km                  float64
preschool_km                             float64
big_church_km                            float64
church_synagogue_km                      float64
mosque_km                                float64
theater_km                               float64
museum_km                                float64
exhibition_km                            float64
catering_km                              float64
ecology                                   object
green_part_500                           float64
prom_part_500                            float64
office_count_500                           int64
office_sqm_500                             int64
trc_count_500                              int64
trc_sqm_500                                int64
cafe_count_500                             int64
cafe_sum_500_min_price_avg               float64
cafe_sum_500_max_price_avg               float64
cafe_avg_price_500                       float64
cafe_count_500_na_price                    int64
cafe_count_500_price_500                   int64
cafe_count_500_price_1000                  int64
cafe_count_500_price_1500                  int64
cafe_count_500_price_2500                  int64
cafe_count_500_price_4000                  int64
cafe_count_500_price_high                  int64
big_church_count_500                       int64
church_count_500                           int64
mosque_count_500                           int64
leisure_count_500                          int64
sport_count_500                            int64
market_count_500                           int64
green_part_1000                          float64
prom_part_1000                           float64
office_count_1000                          int64
office_sqm_1000                            int64
trc_count_1000                             int64
trc_sqm_1000                               int64
cafe_count_1000                            int64
cafe_sum_1000_min_price_avg              float64
cafe_sum_1000_max_price_avg              float64
cafe_avg_price_1000                      float64
cafe_count_1000_na_price                   int64
cafe_count_1000_price_500                  int64
cafe_count_1000_price_1000                 int64
cafe_count_1000_price_1500                 int64
cafe_count_1000_price_2500                 int64
cafe_count_1000_price_4000                 int64
cafe_count_1000_price_high                 int64
big_church_count_1000                      int64
church_count_1000                          int64
mosque_count_1000                          int64
leisure_count_1000                         int64
sport_count_1000                           int64
market_count_1000                          int64
green_part_1500                          float64
prom_part_1500                           float64
office_count_1500                          int64
office_sqm_1500                            int64
trc_count_1500                             int64
trc_sqm_1500                               int64
cafe_count_1500                            int64
cafe_sum_1500_min_price_avg              float64
cafe_sum_1500_max_price_avg              float64
cafe_avg_price_1500                      float64
cafe_count_1500_na_price                   int64
cafe_count_1500_price_500                  int64
cafe_count_1500_price_1000                 int64
cafe_count_1500_price_1500                 int64
cafe_count_1500_price_2500                 int64
cafe_count_1500_price_4000                 int64
cafe_count_1500_price_high                 int64
big_church_count_1500                      int64
church_count_1500                          int64
mosque_count_1500                          int64
leisure_count_1500                         int64
sport_count_1500                           int64
market_count_1500                          int64
green_part_2000                          float64
prom_part_2000                           float64
office_count_2000                          int64
office_sqm_2000                            int64
trc_count_2000                             int64
trc_sqm_2000                               int64
cafe_count_2000                            int64
cafe_sum_2000_min_price_avg              float64
cafe_sum_2000_max_price_avg              float64
cafe_avg_price_2000                      float64
cafe_count_2000_na_price                   int64
cafe_count_2000_price_500                  int64
cafe_count_2000_price_1000                 int64
cafe_count_2000_price_1500                 int64
cafe_count_2000_price_2500                 int64
cafe_count_2000_price_4000                 int64
cafe_count_2000_price_high                 int64
big_church_count_2000                      int64
church_count_2000                          int64
mosque_count_2000                          int64
leisure_count_2000                         int64
sport_count_2000                           int64
market_count_2000                          int64
green_part_3000                          float64
prom_part_3000                           float64
office_count_3000                          int64
office_sqm_3000                            int64
trc_count_3000                             int64
trc_sqm_3000                               int64
cafe_count_3000                            int64
cafe_sum_3000_min_price_avg              float64
cafe_sum_3000_max_price_avg              float64
cafe_avg_price_3000                      float64
cafe_count_3000_na_price                   int64
cafe_count_3000_price_500                  int64
cafe_count_3000_price_1000                 int64
cafe_count_3000_price_1500                 int64
cafe_count_3000_price_2500                 int64
cafe_count_3000_price_4000                 int64
cafe_count_3000_price_high                 int64
big_church_count_3000                      int64
church_count_3000                          int64
mosque_count_3000                          int64
leisure_count_3000                         int64
sport_count_3000                           int64
market_count_3000                          int64
green_part_5000                          float64
prom_part_5000                           float64
office_count_5000                          int64
office_sqm_5000                            int64
trc_count_5000                             int64
trc_sqm_5000                               int64
cafe_count_5000                            int64
cafe_sum_5000_min_price_avg              float64
cafe_sum_5000_max_price_avg              float64
cafe_avg_price_5000                      float64
cafe_count_5000_na_price                   int64
cafe_count_5000_price_500                  int64
cafe_count_5000_price_1000                 int64
cafe_count_5000_price_1500                 int64
cafe_count_5000_price_2500                 int64
cafe_count_5000_price_4000                 int64
cafe_count_5000_price_high                 int64
big_church_count_5000                      int64
church_count_5000                          int64
mosque_count_5000                          int64
leisure_count_5000                         int64
sport_count_5000                           int64
market_count_5000                          int64
price_doc                                  int64
year                                       int64
month                                      int64
day                                        int64
dtype: object

test_data=pd.read_csv("test_data.csv")

test_data["timestamp"][0]

'2015-07-01'

test_data['date']=pd.to_datetime(test_data.timestamp)

test_data.drop("timestamp",axis=1,inplace=True)

test_data['year']=test_data.date.dt.year

test_data['month']=test_data.date.dt.month

test_data['day']=test_data.date.dt.day

test_data.drop("date",axis=1,inplace=True)

test_data.dtypes

id                                         int64
full_sq                                  float64
life_sq                                  float64
floor                                      int64
max_floor                                  int64
material                                   int64
build_year                               float64
num_room                                   int64
kitch_sq                                 float64
state                                    float64
product_type                              object
sub_area                                  object
area_m                                   float64
raion_popul                                int64
green_zone_part                          float64
indust_part                              float64
children_preschool                         int64
preschool_quota                          float64
preschool_education_centers_raion          int64
children_school                            int64
school_quota                             float64
school_education_centers_raion             int64
school_education_centers_top_20_raion      int64
hospital_beds_raion                      float64
healthcare_centers_raion                   int64
university_top_20_raion                    int64
sport_objects_raion                        int64
additional_education_raion                 int64
culture_objects_top_25                    object
culture_objects_top_25_raion               int64
shopping_centers_raion                     int64
office_raion                               int64
thermal_power_plant_raion                 object
incineration_raion                        object
oil_chemistry_raion                       object
radiation_raion                           object
railroad_terminal_raion                   object
big_market_raion                          object
nuclear_reactor_raion                     object
detention_facility_raion                  object
full_all                                   int64
male_f                                     int64
female_f                                   int64
young_all                                  int64
young_male                                 int64
young_female                               int64
work_all                                   int64
work_male                                  int64
work_female                                int64
ekder_all                                  int64
ekder_male                                 int64
ekder_female                               int64
0_6_all                                    int64
0_6_male                                   int64
0_6_female                                 int64
7_14_all                                   int64
7_14_male                                  int64
7_14_female                                int64
0_17_all                                   int64
0_17_male                                  int64
0_17_female                                int64
16_29_all                                  int64
16_29_male                                 int64
16_29_female                               int64
0_13_all                                   int64
0_13_male                                  int64
0_13_female                                int64
raion_build_count_with_material_info     float64
build_count_block                        float64
build_count_wood                         float64
build_count_frame                        float64
build_count_brick                        float64
build_count_monolith                     float64
build_count_panel                        float64
build_count_foam                         float64
build_count_slag                         float64
build_count_mix                          float64
raion_build_count_with_builddate_info    float64
build_count_before_1920                  float64
build_count_1921-1945                    float64
build_count_1946-1970                    float64
build_count_1971-1995                    float64
build_count_after_1995                   float64
ID_metro                                   int64
metro_min_avto                           float64
metro_km_avto                            float64
metro_min_walk                           float64
metro_km_walk                            float64
kindergarten_km                          float64
school_km                                float64
park_km                                  float64
green_zone_km                            float64
industrial_km                            float64
water_treatment_km                       float64
cemetery_km                              float64
incineration_km                          float64
railroad_station_walk_km                 float64
railroad_station_walk_min                float64
ID_railroad_station_walk                 float64
railroad_station_avto_km                 float64
railroad_station_avto_min                float64
ID_railroad_station_avto                   int64
public_transport_station_km              float64
public_transport_station_min_walk        float64
water_km                                 float64
water_1line                               object
mkad_km                                  float64
ttk_km                                   float64
sadovoe_km                               float64
bulvar_ring_km                           float64
kremlin_km                               float64
big_road1_km                             float64
ID_big_road1                               int64
big_road1_1line                           object
big_road2_km                             float64
ID_big_road2                               int64
railroad_km                              float64
railroad_1line                            object
zd_vokzaly_avto_km                       float64
ID_railroad_terminal                       int64
bus_terminal_avto_km                     float64
ID_bus_terminal                            int64
oil_chemistry_km                         float64
nuclear_reactor_km                       float64
radiation_km                             float64
power_transmission_line_km               float64
thermal_power_plant_km                   float64
ts_km                                    float64
big_market_km                            float64
market_shop_km                           float64
fitness_km                               float64
swim_pool_km                             float64
ice_rink_km                              float64
stadium_km                               float64
basketball_km                            float64
hospice_morgue_km                        float64
detention_facility_km                    float64
public_healthcare_km                     float64
university_km                            float64
workplaces_km                            float64
shopping_centers_km                      float64
office_km                                float64
additional_education_km                  float64
preschool_km                             float64
big_church_km                            float64
church_synagogue_km                      float64
mosque_km                                float64
theater_km                               float64
museum_km                                float64
exhibition_km                            float64
catering_km                              float64
ecology                                   object
green_part_500                           float64
prom_part_500                            float64
office_count_500                           int64
office_sqm_500                             int64
trc_count_500                              int64
trc_sqm_500                                int64
cafe_count_500                             int64
cafe_sum_500_min_price_avg               float64
cafe_sum_500_max_price_avg               float64
cafe_avg_price_500                       float64
cafe_count_500_na_price                    int64
cafe_count_500_price_500                   int64
cafe_count_500_price_1000                  int64
cafe_count_500_price_1500                  int64
cafe_count_500_price_2500                  int64
cafe_count_500_price_4000                  int64
cafe_count_500_price_high                  int64
big_church_count_500                       int64
church_count_500                           int64
mosque_count_500                           int64
leisure_count_500                          int64
sport_count_500                            int64
market_count_500                           int64
green_part_1000                          float64
prom_part_1000                           float64
office_count_1000                          int64
office_sqm_1000                            int64
trc_count_1000                             int64
trc_sqm_1000                               int64
cafe_count_1000                            int64
cafe_sum_1000_min_price_avg              float64
cafe_sum_1000_max_price_avg              float64
cafe_avg_price_1000                      float64
cafe_count_1000_na_price                   int64
cafe_count_1000_price_500                  int64
cafe_count_1000_price_1000                 int64
cafe_count_1000_price_1500                 int64
cafe_count_1000_price_2500                 int64
cafe_count_1000_price_4000                 int64
cafe_count_1000_price_high                 int64
big_church_count_1000                      int64
church_count_1000                          int64
mosque_count_1000                          int64
leisure_count_1000                         int64
sport_count_1000                           int64
market_count_1000                          int64
green_part_1500                          float64
prom_part_1500                           float64
office_count_1500                          int64
office_sqm_1500                            int64
trc_count_1500                             int64
trc_sqm_1500                               int64
cafe_count_1500                            int64
cafe_sum_1500_min_price_avg              float64
cafe_sum_1500_max_price_avg              float64
cafe_avg_price_1500                      float64
cafe_count_1500_na_price                   int64
cafe_count_1500_price_500                  int64
cafe_count_1500_price_1000                 int64
cafe_count_1500_price_1500                 int64
cafe_count_1500_price_2500                 int64
cafe_count_1500_price_4000                 int64
cafe_count_1500_price_high                 int64
big_church_count_1500                      int64
church_count_1500                          int64
mosque_count_1500                          int64
leisure_count_1500                         int64
sport_count_1500                           int64
market_count_1500                          int64
green_part_2000                          float64
prom_part_2000                           float64
office_count_2000                          int64
office_sqm_2000                            int64
trc_count_2000                             int64
trc_sqm_2000                               int64
cafe_count_2000                            int64
cafe_sum_2000_min_price_avg              float64
cafe_sum_2000_max_price_avg              float64
cafe_avg_price_2000                      float64
cafe_count_2000_na_price                   int64
cafe_count_2000_price_500                  int64
cafe_count_2000_price_1000                 int64
cafe_count_2000_price_1500                 int64
cafe_count_2000_price_2500                 int64
cafe_count_2000_price_4000                 int64
cafe_count_2000_price_high                 int64
big_church_count_2000                      int64
church_count_2000                          int64
mosque_count_2000                          int64
leisure_count_2000                         int64
sport_count_2000                           int64
market_count_2000                          int64
green_part_3000                          float64
prom_part_3000                           float64
office_count_3000                          int64
office_sqm_3000                            int64
trc_count_3000                             int64
trc_sqm_3000                               int64
cafe_count_3000                            int64
cafe_sum_3000_min_price_avg              float64
cafe_sum_3000_max_price_avg              float64
cafe_avg_price_3000                      float64
cafe_count_3000_na_price                   int64
cafe_count_3000_price_500                  int64
cafe_count_3000_price_1000                 int64
cafe_count_3000_price_1500                 int64
cafe_count_3000_price_2500                 int64
cafe_count_3000_price_4000                 int64
cafe_count_3000_price_high                 int64
big_church_count_3000                      int64
church_count_3000                          int64
mosque_count_3000                          int64
leisure_count_3000                         int64
sport_count_3000                           int64
market_count_3000                          int64
green_part_5000                          float64
prom_part_5000                           float64
office_count_5000                          int64
office_sqm_5000                            int64
trc_count_5000                             int64
trc_sqm_5000                               int64
cafe_count_5000                            int64
cafe_sum_5000_min_price_avg              float64
cafe_sum_5000_max_price_avg              float64
cafe_avg_price_5000                      float64
cafe_count_5000_na_price                   int64
cafe_count_5000_price_500                  int64
cafe_count_5000_price_1000                 int64
cafe_count_5000_price_1500                 int64
cafe_count_5000_price_2500                 int64
cafe_count_5000_price_4000                 int64
cafe_count_5000_price_high                 int64
big_church_count_5000                      int64
church_count_5000                          int64
mosque_count_5000                          int64
leisure_count_5000                         int64
sport_count_5000                           int64
market_count_5000                          int64
year                                       int64
month                                      int64
day                                        int64
dtype: object

train_data.shape

(30471, 294)

test_data.shape

(7662, 293)

Handling Categorical NA values¶

features_nan=[feature for feature in train_data.columns if train_data[feature].isnull().sum()>1 and train_data[feature].dtypes=='O']

for feature in features_nan:
    print("{}: {}% missing values".format(feature,np.round(train_data[feature].isnull().mean(),4)))

features_nan

[]

Handling Numerical NA values¶

numerical_with_nan=[feature for feature in train_data.columns if train_data[feature].isnull().sum()>1 and train_data[feature].dtypes!='O']

for feature in numerical_with_nan:
    print("{}: {}% missing value".format(feature,np.around(train_data[feature].isnull().mean(),4)))

life_sq: 0.2095% missing value
floor: 0.0055% missing value
max_floor: 0.3141% missing value
material: 0.3141% missing value
build_year: 0.4465% missing value
num_room: 0.3141% missing value
kitch_sq: 0.3141% missing value
state: 0.445% missing value
preschool_quota: 0.2195% missing value
school_quota: 0.2194% missing value
hospital_beds_raion: 0.4739% missing value
raion_build_count_with_material_info: 0.1638% missing value
build_count_block: 0.1638% missing value
build_count_wood: 0.1638% missing value
build_count_frame: 0.1638% missing value
build_count_brick: 0.1638% missing value
build_count_monolith: 0.1638% missing value
build_count_panel: 0.1638% missing value
build_count_foam: 0.1638% missing value
build_count_slag: 0.1638% missing value
build_count_mix: 0.1638% missing value
raion_build_count_with_builddate_info: 0.1638% missing value
build_count_before_1920: 0.1638% missing value
build_count_1921-1945: 0.1638% missing value
build_count_1946-1970: 0.1638% missing value
build_count_1971-1995: 0.1638% missing value
build_count_after_1995: 0.1638% missing value
metro_min_walk: 0.0008% missing value
metro_km_walk: 0.0008% missing value
railroad_station_walk_km: 0.0008% missing value
railroad_station_walk_min: 0.0008% missing value
ID_railroad_station_walk: 0.0008% missing value
cafe_sum_500_min_price_avg: 0.4359% missing value
cafe_sum_500_max_price_avg: 0.4359% missing value
cafe_avg_price_500: 0.4359% missing value
cafe_sum_1000_min_price_avg: 0.2141% missing value
cafe_sum_1000_max_price_avg: 0.2141% missing value
cafe_avg_price_1000: 0.2141% missing value
cafe_sum_1500_min_price_avg: 0.1378% missing value
cafe_sum_1500_max_price_avg: 0.1378% missing value
cafe_avg_price_1500: 0.1378% missing value
cafe_sum_2000_min_price_avg: 0.0566% missing value
cafe_sum_2000_max_price_avg: 0.0566% missing value
cafe_avg_price_2000: 0.0566% missing value
cafe_sum_3000_min_price_avg: 0.0325% missing value
cafe_sum_3000_max_price_avg: 0.0325% missing value
cafe_avg_price_3000: 0.0325% missing value
prom_part_5000: 0.0058% missing value
cafe_sum_5000_min_price_avg: 0.0097% missing value
cafe_sum_5000_max_price_avg: 0.0097% missing value
cafe_avg_price_5000: 0.0097% missing value

for feature in numerical_with_nan:
    ## We will replace by using median since there are outliers
    median_value=train_data[feature].median()
    
    ## create a new feature to capture nan values
    train_data[feature+'nan']=np.where(train_data[feature].isnull(),1,0)
    train_data[feature].fillna(median_value,inplace=True)
    
train_data[numerical_with_nan].isnull().sum()

life_sq                                  0
floor                                    0
max_floor                                0
material                                 0
build_year                               0
num_room                                 0
kitch_sq                                 0
state                                    0
preschool_quota                          0
school_quota                             0
hospital_beds_raion                      0
raion_build_count_with_material_info     0
build_count_block                        0
build_count_wood                         0
build_count_frame                        0
build_count_brick                        0
build_count_monolith                     0
build_count_panel                        0
build_count_foam                         0
build_count_slag                         0
build_count_mix                          0
raion_build_count_with_builddate_info    0
build_count_before_1920                  0
build_count_1921-1945                    0
build_count_1946-1970                    0
build_count_1971-1995                    0
build_count_after_1995                   0
metro_min_walk                           0
metro_km_walk                            0
railroad_station_walk_km                 0
railroad_station_walk_min                0
ID_railroad_station_walk                 0
cafe_sum_500_min_price_avg               0
cafe_sum_500_max_price_avg               0
cafe_avg_price_500                       0
cafe_sum_1000_min_price_avg              0
cafe_sum_1000_max_price_avg              0
cafe_avg_price_1000                      0
cafe_sum_1500_min_price_avg              0
cafe_sum_1500_max_price_avg              0
cafe_avg_price_1500                      0
cafe_sum_2000_min_price_avg              0
cafe_sum_2000_max_price_avg              0
cafe_avg_price_2000                      0
cafe_sum_3000_min_price_avg              0
cafe_sum_3000_max_price_avg              0
cafe_avg_price_3000                      0
prom_part_5000                           0
cafe_sum_5000_min_price_avg              0
cafe_sum_5000_max_price_avg              0
cafe_avg_price_5000                      0
dtype: int64

train_data.dtypes

id                                  int64
full_sq                             int64
life_sq                           float64
floor                             float64
max_floor                         float64
                                   ...   
cafe_avg_price_3000nan              int32
prom_part_5000nan                   int32
cafe_sum_5000_min_price_avgnan      int32
cafe_sum_5000_max_price_avgnan      int32
cafe_avg_price_5000nan              int32
Length: 345, dtype: object

features_nan_test=[feature for feature in test_data.columns if test_data[feature].isnull().sum()>1 and test_data[feature].dtypes=='O']

for feature in features_nan_test:
    print("{}: {}% missing values".format(feature,np.round(test_data[feature].isnull().mean(),4)))

product_type: 0.0043% missing values

def replace_cat_feature_test(test_data,features_nan_test):
    data=test_data.copy()
    data[features_nan_test]=data[features_nan_test].fillna('Missing')
    return data

test_data=replace_cat_feature_test(test_data,features_nan_test)

test_data[features_nan_test].isnull().sum()

product_type    0
dtype: int64

numerical_with_nan_test=[feature for feature in test_data.columns if test_data[feature].isnull().sum()>1 and test_data[feature].dtypes!='O']

for feature in numerical_with_nan_test:
    print("{}: {}% missing value".format(feature,np.around(test_data[feature].isnull().mean(),4)))

life_sq: 0.1535% missing value
build_year: 0.1369% missing value
state: 0.0906% missing value
preschool_quota: 0.2083% missing value
school_quota: 0.2082% missing value
hospital_beds_raion: 0.4461% missing value
raion_build_count_with_material_info: 0.159% missing value
build_count_block: 0.159% missing value
build_count_wood: 0.159% missing value
build_count_frame: 0.159% missing value
build_count_brick: 0.159% missing value
build_count_monolith: 0.159% missing value
build_count_panel: 0.159% missing value
build_count_foam: 0.159% missing value
build_count_slag: 0.159% missing value
build_count_mix: 0.159% missing value
raion_build_count_with_builddate_info: 0.159% missing value
build_count_before_1920: 0.159% missing value
build_count_1921-1945: 0.159% missing value
build_count_1946-1970: 0.159% missing value
build_count_1971-1995: 0.159% missing value
build_count_after_1995: 0.159% missing value
metro_min_walk: 0.0044% missing value
metro_km_walk: 0.0044% missing value
railroad_station_walk_km: 0.0044% missing value
railroad_station_walk_min: 0.0044% missing value
ID_railroad_station_walk: 0.0044% missing value
cafe_sum_500_min_price_avg: 0.4123% missing value
cafe_sum_500_max_price_avg: 0.4123% missing value
cafe_avg_price_500: 0.4123% missing value
cafe_sum_1000_min_price_avg: 0.1595% missing value
cafe_sum_1000_max_price_avg: 0.1595% missing value
cafe_avg_price_1000: 0.1595% missing value
cafe_sum_1500_min_price_avg: 0.1072% missing value
cafe_sum_1500_max_price_avg: 0.1072% missing value
cafe_avg_price_1500: 0.1072% missing value
green_part_2000: 0.0025% missing value
cafe_sum_2000_min_price_avg: 0.0553% missing value
cafe_sum_2000_max_price_avg: 0.0553% missing value
cafe_avg_price_2000: 0.0553% missing value
cafe_sum_3000_min_price_avg: 0.0238% missing value
cafe_sum_3000_max_price_avg: 0.0238% missing value
cafe_avg_price_3000: 0.0238% missing value
prom_part_5000: 0.012% missing value
cafe_sum_5000_min_price_avg: 0.0167% missing value
cafe_sum_5000_max_price_avg: 0.0167% missing value
cafe_avg_price_5000: 0.0167% missing value

for feature in numerical_with_nan_test:
    ## We will replace by using median since there are outliers
    median_value=test_data[feature].median()
    
    ## create a new feature to capture nan values
    test_data[feature+'nan']=np.where(test_data[feature].isnull(),1,0)
    test_data[feature].fillna(median_value,inplace=True)
    
test_data[numerical_with_nan_test].isnull().sum()

life_sq                                  0
build_year                               0
state                                    0
preschool_quota                          0
school_quota                             0
hospital_beds_raion                      0
raion_build_count_with_material_info     0
build_count_block                        0
build_count_wood                         0
build_count_frame                        0
build_count_brick                        0
build_count_monolith                     0
build_count_panel                        0
build_count_foam                         0
build_count_slag                         0
build_count_mix                          0
raion_build_count_with_builddate_info    0
build_count_before_1920                  0
build_count_1921-1945                    0
build_count_1946-1970                    0
build_count_1971-1995                    0
build_count_after_1995                   0
metro_min_walk                           0
metro_km_walk                            0
railroad_station_walk_km                 0
railroad_station_walk_min                0
ID_railroad_station_walk                 0
cafe_sum_500_min_price_avg               0
cafe_sum_500_max_price_avg               0
cafe_avg_price_500                       0
cafe_sum_1000_min_price_avg              0
cafe_sum_1000_max_price_avg              0
cafe_avg_price_1000                      0
cafe_sum_1500_min_price_avg              0
cafe_sum_1500_max_price_avg              0
cafe_avg_price_1500                      0
green_part_2000                          0
cafe_sum_2000_min_price_avg              0
cafe_sum_2000_max_price_avg              0
cafe_avg_price_2000                      0
cafe_sum_3000_min_price_avg              0
cafe_sum_3000_max_price_avg              0
cafe_avg_price_3000                      0
prom_part_5000                           0
cafe_sum_5000_min_price_avg              0
cafe_sum_5000_max_price_avg              0
cafe_avg_price_5000                      0
dtype: int64

test_data.shape

(7662, 340)

train_data.shape

(30471, 345)

numerical_features_test= [feature for feature in test_data.columns if test_data[feature].dtypes != 'O']

print('Number of numerical variables: ', len(numerical_features_test))

test_data[numerical_features_test].head()

Number of numerical variables:  325

year_feature_test= [feature for feature in numerical_features if 'Yr' in feature or 'Year' in feature]

year_feature_test

[]

discrete_feature_test=[feature for feature in numerical_features_test if len(test_data[feature].unique())<25 and feature not in year_feature_test+['id']]
print("Discrete Variables Count: {}".format(len(discrete_feature_test)))

Discrete Variables Count: 101

continuous_feature_test=[feature for feature in numerical_features_test if feature not in discrete_feature_test+year_feature_test+['Id']]
print("Continuous feature Count {}".format(len(continuous_feature_test)))

Continuous feature Count 224

categorical_features_test=[feature for feature in test_data.columns if test_data[feature].dtypes=='O']
len(categorical_features_test)

15

for feature in categorical_features_test:
    print('The feature is {} and number of categories are {}'.format(feature,len(test_data[feature].unique())))

The feature is product_type and number of categories are 3
The feature is sub_area and number of categories are 145
The feature is culture_objects_top_25 and number of categories are 2
The feature is thermal_power_plant_raion and number of categories are 2
The feature is incineration_raion and number of categories are 2
The feature is oil_chemistry_raion and number of categories are 2
The feature is radiation_raion and number of categories are 2
The feature is railroad_terminal_raion and number of categories are 2
The feature is big_market_raion and number of categories are 2
The feature is nuclear_reactor_raion and number of categories are 2
The feature is detention_facility_raion and number of categories are 2
The feature is water_1line and number of categories are 2
The feature is big_road1_1line and number of categories are 2
The feature is railroad_1line and number of categories are 2
The feature is ecology and number of categories are 5

RARE VARIABLE¶

for feature in categorical_features_test:
    test_data[feature] = test_data[feature].map((train_data.groupby(feature)['price_doc'].count()/len(train_data)).to_dict())
    #train_data[feature] = train_data[feature].map((train_data.groupby(feature)['SalePrice'].count()/len(train_data)).to_dict())

categorical_features=[feature for feature in train_data.columns if train_data[feature].dtypes=='O' if feature not in ["timestamp"]]
len(categorical_features)

15

for feature in categorical_features:
    temp=train_data.groupby(feature)['price_doc'].count()/len(train_data)
    temp_df=temp[temp>0.01].index
    train_data[feature]=np.where(train_data[feature].isin(temp_df),train_data[feature],'Rare_var')

for feature in categorical_features_test:
    temp=test_data[feature]
    temp_df=temp[temp>0.01].index
    test_data[feature]=np.where(test_data[feature].isin(temp_df),test_data[feature],'Rare_var')

TARGET ENCODING¶

for feature in categorical_features:
    labels_ordered=train_data.groupby([feature])['price_doc'].mean().sort_values().index
    labels_ordered={k:i for i,k in enumerate(labels_ordered,0)}
    train_data[feature]=train_data[feature].map(labels_ordered)

for feature in categorical_features_test:
    labels_ordered=test_data[feature]
    labels_ordered={k:i for i,k in enumerate(labels_ordered,0)}
    test_data[feature]=test_data[feature].map(labels_ordered)

train_data.head()

test_data.head()

Performing log normal distribution on numerical continous variables¶

continuous_feature

['full_sq',
 'life_sq',
 'floor',
 'max_floor',
 'build_year',
 'kitch_sq',
 'area_m',
 'raion_popul',
 'green_zone_part',
 'indust_part',
 'children_preschool',
 'preschool_quota',
 'children_school',
 'school_quota',
 'hospital_beds_raion',
 'office_raion',
 'full_all',
 'male_f',
 'female_f',
 'young_all',
 'young_male',
 'young_female',
 'work_all',
 'work_male',
 'work_female',
 'ekder_all',
 'ekder_male',
 'ekder_female',
 '0_6_all',
 '0_6_male',
 '0_6_female',
 '7_14_all',
 '7_14_male',
 '7_14_female',
 '0_17_all',
 '0_17_male',
 '0_17_female',
 '16_29_all',
 '16_29_male',
 '16_29_female',
 '0_13_all',
 '0_13_male',
 '0_13_female',
 'raion_build_count_with_material_info',
 'build_count_block',
 'build_count_wood',
 'build_count_brick',
 'build_count_monolith',
 'build_count_panel',
 'build_count_before_1920',
 'build_count_1921-1945',
 'build_count_1946-1970',
 'build_count_1971-1995',
 'build_count_after_1995',
 'ID_metro',
 'metro_min_avto',
 'metro_km_avto',
 'metro_min_walk',
 'metro_km_walk',
 'kindergarten_km',
 'school_km',
 'park_km',
 'green_zone_km',
 'industrial_km',
 'water_treatment_km',
 'cemetery_km',
 'incineration_km',
 'railroad_station_walk_km',
 'railroad_station_walk_min',
 'ID_railroad_station_walk',
 'railroad_station_avto_km',
 'railroad_station_avto_min',
 'ID_railroad_station_avto',
 'public_transport_station_km',
 'public_transport_station_min_walk',
 'water_km',
 'mkad_km',
 'ttk_km',
 'sadovoe_km',
 'bulvar_ring_km',
 'kremlin_km',
 'big_road1_km',
 'ID_big_road1',
 'big_road2_km',
 'ID_big_road2',
 'railroad_km',
 'zd_vokzaly_avto_km',
 'bus_terminal_avto_km',
 'oil_chemistry_km',
 'nuclear_reactor_km',
 'radiation_km',
 'power_transmission_line_km',
 'thermal_power_plant_km',
 'ts_km',
 'big_market_km',
 'market_shop_km',
 'fitness_km',
 'swim_pool_km',
 'ice_rink_km',
 'stadium_km',
 'basketball_km',
 'hospice_morgue_km',
 'detention_facility_km',
 'public_healthcare_km',
 'university_km',
 'workplaces_km',
 'shopping_centers_km',
 'office_km',
 'additional_education_km',
 'preschool_km',
 'big_church_km',
 'church_synagogue_km',
 'mosque_km',
 'theater_km',
 'museum_km',
 'exhibition_km',
 'catering_km',
 'green_part_500',
 'prom_part_500',
 'office_count_500',
 'office_sqm_500',
 'trc_sqm_500',
 'cafe_count_500',
 'cafe_sum_500_min_price_avg',
 'cafe_sum_500_max_price_avg',
 'cafe_avg_price_500',
 'cafe_count_500_price_500',
 'cafe_count_500_price_1000',
 'cafe_count_500_price_1500',
 'green_part_1000',
 'prom_part_1000',
 'office_count_1000',
 'office_sqm_1000',
 'trc_sqm_1000',
 'cafe_count_1000',
 'cafe_sum_1000_min_price_avg',
 'cafe_sum_1000_max_price_avg',
 'cafe_avg_price_1000',
 'cafe_count_1000_na_price',
 'cafe_count_1000_price_500',
 'cafe_count_1000_price_1000',
 'cafe_count_1000_price_1500',
 'cafe_count_1000_price_2500',
 'cafe_count_1000_price_4000',
 'church_count_1000',
 'leisure_count_1000',
 'green_part_1500',
 'prom_part_1500',
 'office_count_1500',
 'office_sqm_1500',
 'trc_count_1500',
 'trc_sqm_1500',
 'cafe_count_1500',
 'cafe_sum_1500_min_price_avg',
 'cafe_sum_1500_max_price_avg',
 'cafe_avg_price_1500',
 'cafe_count_1500_na_price',
 'cafe_count_1500_price_500',
 'cafe_count_1500_price_1000',
 'cafe_count_1500_price_1500',
 'cafe_count_1500_price_2500',
 'cafe_count_1500_price_4000',
 'big_church_count_1500',
 'church_count_1500',
 'leisure_count_1500',
 'sport_count_1500',
 'green_part_2000',
 'prom_part_2000',
 'office_count_2000',
 'office_sqm_2000',
 'trc_count_2000',
 'trc_sqm_2000',
 'cafe_count_2000',
 'cafe_sum_2000_min_price_avg',
 'cafe_sum_2000_max_price_avg',
 'cafe_avg_price_2000',
 'cafe_count_2000_na_price',
 'cafe_count_2000_price_500',
 'cafe_count_2000_price_1000',
 'cafe_count_2000_price_1500',
 'cafe_count_2000_price_2500',
 'cafe_count_2000_price_4000',
 'big_church_count_2000',
 'church_count_2000',
 'leisure_count_2000',
 'sport_count_2000',
 'green_part_3000',
 'prom_part_3000',
 'office_count_3000',
 'office_sqm_3000',
 'trc_count_3000',
 'trc_sqm_3000',
 'cafe_count_3000',
 'cafe_sum_3000_min_price_avg',
 'cafe_sum_3000_max_price_avg',
 'cafe_avg_price_3000',
 'cafe_count_3000_na_price',
 'cafe_count_3000_price_500',
 'cafe_count_3000_price_1000',
 'cafe_count_3000_price_1500',
 'cafe_count_3000_price_2500',
 'cafe_count_3000_price_4000',
 'big_church_count_3000',
 'church_count_3000',
 'leisure_count_3000',
 'sport_count_3000',
 'green_part_5000',
 'prom_part_5000',
 'office_count_5000',
 'office_sqm_5000',
 'trc_count_5000',
 'trc_sqm_5000',
 'cafe_count_5000',
 'cafe_sum_5000_min_price_avg',
 'cafe_sum_5000_max_price_avg',
 'cafe_avg_price_5000',
 'cafe_count_5000_na_price',
 'cafe_count_5000_price_500',
 'cafe_count_5000_price_1000',
 'cafe_count_5000_price_1500',
 'cafe_count_5000_price_2500',
 'cafe_count_5000_price_4000',
 'cafe_count_5000_price_high',
 'big_church_count_5000',
 'church_count_5000',
 'leisure_count_5000',
 'sport_count_5000',
 'price_doc']

import numpy as np
num_features=continuous_feature

for feature in num_features:
    train_data[feature]=np.log(train_data[feature])

for feature in continuous_feature_test:
    data=test_data.copy()
    if 0 in data[feature].unique():
        pass
    else:
        data[feature]=np.log(data[feature])
        #data['SalePrice']=np.log(data['SalePrice'])
        #plt.scatter(data[feature],data['SalePrice'])
        plt.xlabel(feature)
        plt.ylabel('SalesPrice')
        plt.title(feature)
        plt.show()

import numpy as np
num_features=continuous_feature_test

for feature in num_features:
    train_data[feature]=np.log(train_data[feature])

train_data.head()

FEATURE SCALING¶

scaling_feature=[feature for feature in train_data.columns if feature not in ['id','Price_doc'] ]
len(scaling_feature)

344

train_data.fillna(0,inplace=True)

[min(train_data[i]) for i in train_data.columns]

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 -inf,
 -6.568681126087842,
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 -inf,
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 -inf,
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 -inf,
 -10.516654798246318,
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 5,
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 1,
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 -5.4671174105889655,
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 -6.767786208227923,
 -8.924018212329587,
 -8.237162889211437,
 -8.617352647072467,
 -13.60120657469366,
 -6.306148670262413,
 -7.819587781838355,
 -10.42407538206976,
 -6.162857867528065,
 -10.311111584483152,
 -6.34014968366858,
 -8.100425642082211,
 -8.375370596575328,
 -9.921350268163124,
 -10.487614911910729,
 -7.363242555871257,
 -8.55392245119615,
 -8.207960479608374,
 -7.777596203159332,
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 0,
 -inf,
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 1.8269026656007323,
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 -inf,
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 -4.610149476789775,
 -inf,
 -inf,
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 -inf,
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 1.790335880924894,
 -inf,
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 -inf,
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 -inf,
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 -2.5644559444400956,
 -inf,
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 -inf,
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 -inf,
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 0,
 -1.9678147153196068,
 -inf,
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 -inf,
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 -inf,
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 -inf,
 -inf,
 -inf,
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 -inf,
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 -inf,
 -inf,
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 0.22988953758400052,
 -inf,
 -inf,
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 -inf,
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 0,
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 0,
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 0,
 0,
 0,
 0,
 0,
 0]

for i in train_data.columns:
    train_data.loc[(train_data[i]==min(train_data[i])),i]=np.quantile(train_data[i],1)

from sklearn.preprocessing import StandardScaler
scaler=StandardScaler()
train_data.loc[:,scaling_feature] = scaler.fit_transform(train_data.loc[:,scaling_feature])

scaling_feature_test=[feature for feature in test_data.columns if feature not in ['id'] ]
len(scaling_feature_test)

339

for i in test_data.columns:
    test_data.loc[(test_data[i]==min(test_data[i])),i]=np.quantile(test_data[i],1)

from sklearn.preprocessing import StandardScaler
scaler=StandardScaler()
test_data.loc[:,scaling_feature_test] = scaler.fit_transform(test_data.loc[:,scaling_feature_test])

test_data.head()

FEATURE SELECTION¶

from sklearn.linear_model import Lasso
from sklearn.feature_selection import SelectFromModel

train_data.drop("id",axis=1,inplace=True)

train_data_new=train_data.drop(['price_doc'],axis=1)

y_train=train_data[['price_doc']]

feature_sel_model = SelectFromModel(Lasso(alpha=0.005, random_state=123)) # remember to set the seed, the random state in this function
feature_sel_model.fit(train_data_new, y_train)

SelectFromModel(estimator=Lasso(alpha=0.005, copy_X=True, fit_intercept=True,
                                max_iter=1000, normalize=False, positive=False,
                                precompute=False, random_state=123,
                                selection='cyclic', tol=0.0001,
                                warm_start=False),
                max_features=None, norm_order=1, prefit=False, threshold=None)

selected_feat = train_data_new.columns[(feature_sel_model.get_support())]

# let's print some stats
print('total features: {}'.format((train_data_new.shape[1])))
print('selected features: {}'.format(len(selected_feat)))
print('features with coefficients shrank to zero: {}'.format(np.sum(feature_sel_model.estimator_.coef_ == 0)))

total features: 343
selected features: 120
features with coefficients shrank to zero: 223

selected_feat

Index(['full_sq', 'life_sq', 'max_floor', 'material', 'build_year', 'num_room',
       'kitch_sq', 'state', 'sub_area', 'preschool_quota',
       ...
       'cafe_sum_5000_max_price_avg', 'cafe_count_5000_price_500',
       'cafe_count_5000_price_1000', 'cafe_count_5000_price_1500',
       'cafe_count_5000_price_2500', 'cafe_count_5000_price_4000',
       'cafe_count_5000_price_high', 'leisure_count_5000', 'sport_count_5000',
       'year'],
      dtype='object', length=120)

train_data_new=train_data_new[selected_feat]

test_data.to_csv("test123.csv")

test_data_new=pd.read_csv("test123.csv",usecols=selected_feat)

test_data_new.shape

(7662, 120)

train_data_new.shape

(30471, 120)

from sklearn.model_selection import train_test_split

xtrain,xtest,ytrain,ytest=train_test_split(train_data_new,y_train,test_size=0.3,random_state=123)

xtrain.shape,xtest.shape,ytrain.shape,ytest.shape

((21329, 120), (9142, 120), (21329, 1), (9142, 1))

ytrain=np.array(ytrain)
ytest = np.array(ytest)

def model_builder(model):
    m = model
    m.fit(xtrain,ytrain) # log(y) follows normal distribution
    train_pred =np.exp(m.predict(xtrain))
    test_pred = np.exp(m.predict(xtest))
    print('========Train=======')
    print('RMSE :' ,np.sqrt(mean_squared_error(ytrain,train_pred)))
    print('MAPE :' , np.mean(np.abs((ytrain-train_pred)/ytrain))*100)
    print('========Test=======')
    print('RMSE :' ,np.sqrt(mean_squared_error(ytest,test_pred)))
    print('MAPE :' , np.mean(np.abs((ytest-test_pred)/ytest))*100)
    return m

train_data_new.fillna(0,inplace=True)

np.where(train_data_new.values >= np.finfo(np.float64).max)

(array([], dtype=int64), array([], dtype=int64))

lr=model_builder(LinearRegression())

========Train=======
RMSE : 1.9566156934714765
MAPE : 1173.0115037051728
========Test=======
RMSE : 2.168163042526417
MAPE : 892.5329576393402

xg = model_builder(XGBRegressor())

========Train=======
RMSE : 2.4013148581991
MAPE : 1673.4218221945025
========Test=======
RMSE : 2.185605323077104
MAPE : 1338.9667586534404

xg = model_builder(XGBRegressor())

========Train=======
RMSE : 2.4013148581991
MAPE : 1673.4218221945025
========Test=======
RMSE : 2.185605323077104
MAPE : 1338.9667586534404

params ={'max_depth':range(1,15), 'min_samples_split':range(10,70,10)}
 
dt = model_builder(GridSearchCV(DecisionTreeRegressor( ) , param_grid=params , cv=2 , n_jobs=-1))

========Train=======
RMSE : 1.7590339520454807
MAPE : 1473.3109878439675
========Test=======
RMSE : 1.8057512588993503
MAPE : 1238.9236506498446

params = {'n_estimators':range(50,200,50),'learning_rate':[0.5,0.7,1.0]}
ad = model_builder(GridSearchCV(AdaBoostRegressor() , param_grid=params , cv=2 ,n_jobs=-1))

========Train=======
RMSE : 1.0519314655659922
MAPE : 645.4961496920954
========Test=======
RMSE : 1.081722131068907
MAPE : 544.9429240394156

gb = model_builder(GradientBoostingRegressor(n_estimators=50 , learning_rate=0.1))

========Train=======
RMSE : 1.602944277401274
MAPE : 1432.9728593461186
========Test=======
RMSE : 1.626682221055294
MAPE : 1198.0175654116974

from sklearn.linear_model import Ridge
from sklearn.linear_model import Lasso

clf=model_builder(Ridge())

========Train=======
RMSE : 1.9563973670194497
MAPE : 1172.9959597401967
========Test=======
RMSE : 2.167822260676223
MAPE : 892.5289066706323

clf2=model_builder(Lasso())

========Train=======
RMSE : 1.4105768207813816
MAPE : 1169.3184292169735
========Test=======
RMSE : 1.43518690681185
MAPE : 983.7045381218818

rf123=model_builder(RandomForestRegressor())

========Train=======
RMSE : 2.1293767687410385
MAPE : 1672.4426218161948
========Test=======
RMSE : 1.845423825621202
MAPE : 1265.5462614173314

sub=pd.read_csv("sample_submission.csv/sample_submission.csv")

sub.head()

test_prediction = np.exp(rf123.predict(test_data_new))

sub['price_doc']=test_prediction

sub.to_csv('final_submission_rf34.csv',index=False)

	id	full_sq	life_sq	floor	max_floor	material	build_year	num_room	kitch_sq	state	area_m	raion_popul	green_zone_part	indust_part	children_preschool	preschool_quota	preschool_education_centers_raion	children_school	school_quota	school_education_centers_raion	hospital_beds_raion	healthcare_centers_raion	university_top_20_raion	sport_objects_raion	additional_education_raion	culture_objects_top_25_raion	shopping_centers_raion	office_raion	full_all	male_f	female_f	young_all	young_male	young_female	work_all	work_male	work_female	ekder_all	ekder_male	ekder_female	0_6_all	0_6_male	0_6_female	7_14_all	7_14_male	7_14_female	0_17_all	0_17_male	0_17_female	16_29_all	16_29_male	16_29_female	0_13_all	0_13_male	0_13_female	raion_build_count_with_material_info	build_count_block	build_count_wood	build_count_frame	build_count_brick	build_count_monolith	build_count_panel	build_count_slag	build_count_mix	raion_build_count_with_builddate_info	build_count_before_1920	build_count_1921-1945	build_count_1946-1970	build_count_1971-1995	build_count_after_1995	ID_metro	metro_min_avto	metro_km_avto	metro_min_walk	metro_km_walk	kindergarten_km	school_km	park_km	green_zone_km	industrial_km	water_treatment_km	cemetery_km	incineration_km	railroad_station_walk_km	railroad_station_walk_min	ID_railroad_station_walk	railroad_station_avto_km	railroad_station_avto_min	ID_railroad_station_avto	public_transport_station_km	public_transport_station_min_walk	water_km	mkad_km	ttk_km	sadovoe_km	bulvar_ring_km	kremlin_km	big_road1_km	ID_big_road1	big_road2_km	ID_big_road2	railroad_km	zd_vokzaly_avto_km	ID_railroad_terminal	bus_terminal_avto_km	ID_bus_terminal	oil_chemistry_km	nuclear_reactor_km	radiation_km	power_transmission_line_km	thermal_power_plant_km	ts_km	big_market_km	market_shop_km	fitness_km	swim_pool_km	ice_rink_km	stadium_km	basketball_km	hospice_morgue_km	detention_facility_km	public_healthcare_km	university_km	workplaces_km	shopping_centers_km	office_km	additional_education_km	preschool_km	big_church_km	church_synagogue_km	mosque_km	theater_km	museum_km	exhibition_km	catering_km	green_part_500	prom_part_500	office_count_500	office_sqm_500	trc_count_500	trc_sqm_500	cafe_count_500	cafe_sum_500_min_price_avg	cafe_sum_500_max_price_avg	cafe_avg_price_500	cafe_count_500_na_price	cafe_count_500_price_500	cafe_count_500_price_1000	cafe_count_500_price_1500	cafe_count_500_price_2500	cafe_count_500_price_4000	big_church_count_500	church_count_500	leisure_count_500	sport_count_500	green_part_1000	prom_part_1000	office_count_1000	office_sqm_1000	trc_count_1000	trc_sqm_1000	cafe_count_1000	cafe_sum_1000_min_price_avg	cafe_sum_1000_max_price_avg	cafe_avg_price_1000	cafe_count_1000_na_price	cafe_count_1000_price_500	cafe_count_1000_price_1000	cafe_count_1000_price_1500	cafe_count_1000_price_2500	cafe_count_1000_price_4000	cafe_count_1000_price_high	big_church_count_1000	church_count_1000	leisure_count_1000	sport_count_1000	market_count_1000	green_part_1500	prom_part_1500	office_count_1500	office_sqm_1500	trc_count_1500	trc_sqm_1500	cafe_count_1500	cafe_sum_1500_min_price_avg	cafe_sum_1500_max_price_avg	cafe_avg_price_1500	cafe_count_1500_na_price	cafe_count_1500_price_500	cafe_count_1500_price_1000	cafe_count_1500_price_1500	cafe_count_1500_price_2500	cafe_count_1500_price_4000	cafe_count_1500_price_high	big_church_count_1500	church_count_1500	leisure_count_1500	sport_count_1500	market_count_1500	green_part_2000	prom_part_2000	office_count_2000	office_sqm_2000	trc_count_2000	trc_sqm_2000	cafe_count_2000	cafe_sum_2000_min_price_avg	cafe_sum_2000_max_price_avg	cafe_avg_price_2000	cafe_count_2000_na_price	cafe_count_2000_price_500	cafe_count_2000_price_1000	cafe_count_2000_price_1500	cafe_count_2000_price_2500	cafe_count_2000_price_4000	cafe_count_2000_price_high	big_church_count_2000	church_count_2000	leisure_count_2000	sport_count_2000	market_count_2000	green_part_3000	prom_part_3000	office_count_3000	office_sqm_3000	trc_count_3000	trc_sqm_3000	cafe_count_3000	cafe_sum_3000_min_price_avg	cafe_sum_3000_max_price_avg	cafe_avg_price_3000	cafe_count_3000_na_price	cafe_count_3000_price_500	cafe_count_3000_price_1000	cafe_count_3000_price_1500	cafe_count_3000_price_2500	cafe_count_3000_price_4000	cafe_count_3000_price_high	big_church_count_3000	church_count_3000	mosque_count_3000	leisure_count_3000	sport_count_3000	market_count_3000	green_part_5000	prom_part_5000	office_count_5000	office_sqm_5000	trc_count_5000	trc_sqm_5000	cafe_count_5000	cafe_sum_5000_min_price_avg	cafe_sum_5000_max_price_avg	cafe_avg_price_5000	cafe_count_5000_na_price	cafe_count_5000_price_500	cafe_count_5000_price_1000	cafe_count_5000_price_1500	cafe_count_5000_price_2500	cafe_count_5000_price_4000	cafe_count_5000_price_high	big_church_count_5000	church_count_5000	mosque_count_5000	leisure_count_5000	sport_count_5000	market_count_5000	price_doc
0	1	43	27.0	4.0	NaN	NaN	NaN	NaN	NaN	NaN	6.407578e+06	155572	0.189727	0.000070	9576	5001.0	5	10309	11065.0	5	240.0	1	0	7	3	0	16	1	86206	40477	45729	21154	11007	10147	98207	52277	45930	36211	10580	25631	9576	4899	4677	10309	5463	4846	23603	12286	11317	17508	9425	8083	18654	9709	8945	211.0	25.0	0.0	0.0	0.0	2.0	184.0	0.0	0.0	211.0	0.0	0.0	0.0	206.0	5.0	1	2.590241	1.131260	13.575119	1.131260	0.145700	0.177975	2.158587	0.600973	1.080934	23.683460	1.804127	3.633334	5.419893	65.038716	1.0	5.419893	6.905893	1	0.274985	3.299822	0.992631	1.422391	10.918587	13.100618	13.675657	15.156211	1.422391	1	3.830951	5	1.305159	14.231961	101	24.292406	1	18.152338	5.718519	1.210027	1.062513	5.814135	4.308127	10.814172	1.676258	0.485841	3.065047	1.107594	8.148591	3.516513	2.392353	4.248036	0.974743	6.715026	0.884350	0.648488	0.637189	0.947962	0.177975	0.625783	0.628187	3.932040	14.053047	7.389498	7.023705	0.516838	0.00	0.00	0	0	0	0	0	NaN	NaN	NaN	0	0	0	0	0	0	0	0	0	1	7.36	0.00	1	30500	3	55600	19	527.78	888.89	708.33	1	10	4	3	1	0	0	1	2	0	6	1	14.27	6.92	3	39554	9	171420	34	566.67	969.70	768.18	1	14	11	6	2	0	0	1	2	0	7	1	11.77	15.97	9	188854	19	1244891	36	614.29	1042.86	828.57	1	15	11	6	2	1	0	1	2	0	10	1	11.98	13.55	12	251554	23	1419204	68	639.68	1079.37	859.52	5	21	22	16	3	1	0	2	4	0	0	21	1	13.09	13.31	29	807385	52	4036616	152	708.57	1185.71	947.14	12	39	48	40	9	4	0	13	22	1	0	52	4	5850000
1	2	34	19.0	3.0	NaN	NaN	NaN	NaN	NaN	NaN	9.589337e+06	115352	0.372602	0.049637	6880	3119.0	5	7759	6237.0	8	229.0	1	0	6	1	1	3	0	76284	34200	42084	15727	7925	7802	70194	35622	34572	29431	9266	20165	6880	3466	3414	7759	3909	3850	17700	8998	8702	15164	7571	7593	13729	6929	6800	245.0	83.0	1.0	0.0	67.0	4.0	90.0	0.0	0.0	244.0	1.0	1.0	143.0	84.0	15.0	2	0.936700	0.647337	7.620630	0.635053	0.147754	0.273345	0.550690	0.065321	0.966479	1.317476	4.655004	8.648587	3.411993	40.943917	2.0	3.641773	4.679745	2	0.065263	0.783160	0.698081	9.503405	3.103996	6.444333	8.132640	8.698054	2.887377	2	3.103996	4	0.694536	9.242586	32	5.706113	2	9.034642	3.489954	2.724295	1.246149	3.419574	0.725560	6.910568	3.424716	0.668364	2.000154	8.972823	6.127073	1.161579	2.543747	12.649879	1.477723	1.852560	0.686252	0.519311	0.688796	1.072315	0.273345	0.967821	0.471447	4.841544	6.829889	0.709260	2.358840	0.230287	25.14	0.00	0	0	0	0	5	860.00	1500.00	1180.00	0	1	3	0	0	1	0	1	0	0	26.66	0.07	2	86600	5	94065	13	615.38	1076.92	846.15	0	5	6	1	0	1	0	1	2	4	2	0	21.53	7.71	3	102910	7	127065	17	694.12	1205.88	950.00	0	6	7	1	2	1	0	1	5	4	9	0	22.37	19.25	4	165510	8	179065	21	695.24	1190.48	942.86	0	7	8	3	2	1	0	1	5	4	11	0	18.07	27.32	12	821986	14	491565	30	631.03	1086.21	858.62	1	11	11	4	2	1	0	1	7	0	6	19	1	10.26	27.47	66	2690465	40	2034942	177	673.81	1148.81	911.31	9	49	65	36	15	3	0	15	29	1	10	66	14	6000000
2	3	43	29.0	2.0	NaN	NaN	NaN	NaN	NaN	NaN	4.808270e+06	101708	0.112560	0.118537	5879	1463.0	4	6207	5580.0	7	1183.0	1	0	5	1	0	0	1	101982	46076	55906	13028	6835	6193	63388	31813	31575	25292	7609	17683	5879	3095	2784	6207	3269	2938	14884	7821	7063	19401	9045	10356	11252	5916	5336	330.0	59.0	0.0	0.0	206.0	4.0	60.0	1.0	0.0	330.0	1.0	0.0	246.0	63.0	20.0	3	2.120999	1.637996	17.351515	1.445960	0.049102	0.158072	0.374848	0.453172	0.939275	4.912660	3.381083	11.996480	1.277658	15.331896	3.0	1.277658	1.701420	3	0.328756	3.945073	0.468265	5.604800	2.927487	6.963403	8.054252	9.067885	0.647250	3	2.927487	4	0.700691	9.540544	5	6.710302	3	5.777394	7.506612	0.772216	1.602183	3.682455	3.562188	5.752368	1.375443	0.733101	1.239304	1.978517	0.767569	1.952771	0.621357	7.682303	0.097144	0.841254	1.510089	1.486533	1.543049	0.391957	0.158072	3.178751	0.755946	7.922152	4.273200	3.156423	4.958214	0.190462	1.67	0.00	0	0	0	0	3	666.67	1166.67	916.67	0	0	2	1	0	0	0	0	0	0	4.99	0.29	0	0	0	0	9	642.86	1142.86	892.86	2	0	5	2	0	0	0	0	1	0	5	3	9.92	6.73	0	0	1	2600	14	516.67	916.67	716.67	2	4	6	2	0	0	0	0	4	0	6	5	12.99	12.75	4	100200	7	52550	24	563.64	977.27	770.45	2	8	9	4	1	0	0	0	4	0	8	5	12.14	26.46	8	110856	7	52550	41	697.44	1192.31	944.87	2	9	17	9	3	1	0	0	11	0	0	20	6	13.69	21.58	43	1478160	35	1572990	122	702.68	1196.43	949.55	10	29	45	25	10	3	0	11	27	0	4	67	10	5700000
3	4	89	50.0	9.0	NaN	NaN	NaN	NaN	NaN	NaN	1.258354e+07	178473	0.194703	0.069753	13087	6839.0	9	13670	17063.0	10	NaN	1	0	17	6	0	11	4	21155	9828	11327	28563	14680	13883	120381	60040	60341	29529	9083	20446	13087	6645	6442	13670	7126	6544	32063	16513	15550	3292	1450	1842	24934	12782	12152	458.0	9.0	51.0	12.0	124.0	50.0	201.0	9.0	2.0	459.0	13.0	24.0	40.0	130.0	252.0	4	1.489049	0.984537	11.565624	0.963802	0.179441	0.236455	0.078090	0.106125	0.451173	15.623710	2.017080	14.317640	4.291432	51.497190	4.0	3.816045	5.271136	4	0.131597	1.579164	1.200336	2.677824	14.606501	17.457198	18.309433	19.487005	2.677824	1	2.780449	17	1.999265	17.478380	83	6.734618	1	27.667863	9.522538	6.348716	1.767612	11.178333	0.583025	27.892717	0.811275	0.623484	1.950317	6.483172	7.385521	4.923843	3.549558	8.789894	2.163735	10.903161	0.622272	0.599914	0.934273	0.892674	0.236455	1.031777	1.561505	15.300449	16.990677	16.041521	5.029696	0.465820	17.36	0.57	0	0	0	0	2	1000.00	1500.00	1250.00	0	0	0	2	0	0	0	0	0	0	19.25	10.35	1	11000	6	80780	12	658.33	1083.33	870.83	0	3	4	5	0	0	0	0	0	0	3	1	28.38	6.57	2	11000	7	89492	23	673.91	1130.43	902.17	0	5	9	8	1	0	0	1	0	0	9	2	32.29	5.73	2	11000	7	89492	25	660.00	1120.00	890.00	0	5	11	8	1	0	0	1	1	0	13	2	20.79	3.57	4	167000	12	205756	32	718.75	1218.75	968.75	0	5	14	10	3	0	0	1	2	0	0	18	3	14.18	3.89	8	244166	22	942180	61	931.58	1552.63	1242.11	4	7	21	15	11	2	1	4	4	0	0	26	3	13100000
4	5	77	77.0	4.0	NaN	NaN	NaN	NaN	NaN	NaN	8.398461e+06	108171	0.015234	0.037316	5706	3240.0	7	6748	7770.0	9	562.0	4	2	25	2	0	10	93	28179	13522	14657	13368	7159	6209	68043	34236	33807	26760	8563	18197	5706	2982	2724	6748	3664	3084	15237	8113	7124	5164	2583	2581	11631	6223	5408	746.0	48.0	0.0	0.0	643.0	16.0	35.0	3.0	1.0	746.0	371.0	114.0	146.0	62.0	53.0	5	1.257186	0.876620	8.266305	0.688859	0.247901	0.376838	0.258289	0.236214	0.392871	10.683540	2.936581	11.903910	0.853960	10.247521	5.0	1.595898	2.156284	113	0.071480	0.857764	0.820294	11.616653	1.721834	0.046810	0.787593	2.578671	1.721834	4	3.133531	10	0.084113	1.595898	113	1.423428	4	6.515857	8.671016	1.638318	3.632640	4.587917	2.609420	9.155057	1.969738	0.220288	2.544696	3.975401	3.610754	0.307915	1.864637	3.779781	1.121703	0.991683	0.892668	0.429052	0.077901	0.810801	0.376838	0.378756	0.121681	2.584370	1.112486	1.800125	1.339652	0.026102	3.56	4.44	15	293699	1	45000	48	702.22	1166.67	934.44	3	17	10	11	7	0	1	4	2	3	3.34	8.29	46	420952	3	158200	153	763.45	1272.41	1017.93	8	39	45	39	19	2	1	7	12	6	7	0	4.12	4.83	93	1195735	9	445900	272	766.80	1272.73	1019.76	19	70	74	72	30	6	1	18	30	10	14	2	4.53	5.02	149	1625130	17	564843	483	765.93	1269.23	1017.58	28	130	129	131	50	14	1	35	61	17	21	3	5.06	8.62	305	3420907	60	2296870	1068	853.03	1410.45	1131.74	63	266	267	262	149	57	4	70	121	1	40	77	5	8.38	10.92	689	8404624	114	3503058	2283	853.88	1411.45	1132.66	143	566	578	552	319	108	17	135	236	2	91	195	14	16331452

	id	full_sq	life_sq	floor	max_floor	material	build_year	num_room	kitch_sq	state	area_m	raion_popul	green_zone_part	indust_part	children_preschool	preschool_quota	preschool_education_centers_raion	children_school	school_quota	school_education_centers_raion	school_education_centers_top_20_raion	hospital_beds_raion	healthcare_centers_raion	sport_objects_raion	additional_education_raion	shopping_centers_raion	office_raion	full_all	male_f	female_f	young_all	young_male	young_female	work_all	work_male	work_female	ekder_all	ekder_male	ekder_female	0_6_all	0_6_male	0_6_female	7_14_all	7_14_male	7_14_female	0_17_all	0_17_male	0_17_female	16_29_all	16_29_male	16_29_female	0_13_all	0_13_male	0_13_female	raion_build_count_with_material_info	build_count_block	build_count_wood	build_count_frame	build_count_brick	build_count_monolith	build_count_panel	build_count_foam	build_count_slag	build_count_mix	raion_build_count_with_builddate_info	build_count_before_1920	build_count_1921-1945	build_count_1946-1970	build_count_1971-1995	build_count_after_1995	ID_metro	metro_min_avto	metro_km_avto	metro_min_walk	metro_km_walk	kindergarten_km	school_km	park_km	green_zone_km	industrial_km	water_treatment_km	cemetery_km	incineration_km	railroad_station_walk_km	railroad_station_walk_min	ID_railroad_station_walk	railroad_station_avto_km	railroad_station_avto_min	ID_railroad_station_avto	public_transport_station_km	public_transport_station_min_walk	water_km	mkad_km	ttk_km	sadovoe_km	bulvar_ring_km	kremlin_km	big_road1_km	ID_big_road1	big_road2_km	ID_big_road2	railroad_km	zd_vokzaly_avto_km	ID_railroad_terminal	bus_terminal_avto_km	ID_bus_terminal	oil_chemistry_km	nuclear_reactor_km	radiation_km	power_transmission_line_km	thermal_power_plant_km	ts_km	big_market_km	market_shop_km	fitness_km	swim_pool_km	ice_rink_km	stadium_km	basketball_km	hospice_morgue_km	detention_facility_km	public_healthcare_km	university_km	workplaces_km	shopping_centers_km	office_km	additional_education_km	preschool_km	big_church_km	church_synagogue_km	mosque_km	theater_km	museum_km	exhibition_km	catering_km	green_part_500	prom_part_500	cafe_count_500	cafe_sum_500_min_price_avg	cafe_sum_500_max_price_avg	cafe_avg_price_500	cafe_count_500_na_price	cafe_count_500_price_500	cafe_count_500_price_1000	...	big_church_count_1000	church_count_1000	leisure_count_1000	sport_count_1000	market_count_1000	green_part_1500	prom_part_1500	office_count_1500	office_sqm_1500	trc_count_1500	cafe_count_1500	cafe_sum_1500_min_price_avg	cafe_sum_1500_max_price_avg	cafe_avg_price_1500	cafe_count_1500_na_price	cafe_count_1500_price_500	cafe_count_1500_price_1000	cafe_count_1500_price_1500	cafe_count_1500_price_2500	big_church_count_1500	church_count_1500	leisure_count_1500	sport_count_1500	market_count_1500	green_part_2000	prom_part_2000	office_count_2000	office_sqm_2000	trc_count_2000	trc_sqm_2000	cafe_count_2000	cafe_sum_2000_min_price_avg	cafe_sum_2000_max_price_avg	cafe_avg_price_2000	cafe_count_2000_na_price	cafe_count_2000_price_500	cafe_count_2000_price_1000	cafe_count_2000_price_1500	cafe_count_2000_price_2500	big_church_count_2000	church_count_2000	mosque_count_2000	leisure_count_2000	sport_count_2000	market_count_2000	green_part_3000	prom_part_3000	office_count_3000	office_sqm_3000	trc_count_3000	trc_sqm_3000	cafe_count_3000	cafe_sum_3000_min_price_avg	cafe_sum_3000_max_price_avg	cafe_avg_price_3000	cafe_count_3000_na_price	cafe_count_3000_price_500	cafe_count_3000_price_1000	cafe_count_3000_price_1500	cafe_count_3000_price_2500	big_church_count_3000	church_count_3000	mosque_count_3000	leisure_count_3000	sport_count_3000	market_count_3000	green_part_5000	prom_part_5000	office_count_5000	office_sqm_5000	trc_count_5000	trc_sqm_5000	cafe_count_5000	cafe_sum_5000_min_price_avg	cafe_sum_5000_max_price_avg	cafe_avg_price_5000	cafe_count_5000_na_price	cafe_count_5000_price_500	cafe_count_5000_price_1000	cafe_count_5000_price_1500	cafe_count_5000_price_2500	cafe_count_5000_price_4000	big_church_count_5000	church_count_5000	mosque_count_5000	leisure_count_5000	sport_count_5000	market_count_5000	year	month	day	life_sqnan	preschool_quotanan	school_quotanan	hospital_beds_raionnan	raion_build_count_with_material_infonan	build_count_blocknan	build_count_woodnan	build_count_framenan	build_count_bricknan	build_count_monolithnan	build_count_panelnan	build_count_foamnan	build_count_slagnan	build_count_mixnan	raion_build_count_with_builddate_infonan	build_count_before_1920nan	build_count_1921-1945nan	build_count_1946-1970nan	build_count_1971-1995nan	build_count_after_1995nan	cafe_sum_500_min_price_avgnan	cafe_sum_500_max_price_avgnan	cafe_avg_price_500nan
0	30474	39.0	20.7	2	9	1	1998.0	1	8.9	3.0	2.615514e+07	178264	0.137846	0.041116	14080	11926.0	11	14892	24750.0	13	1	990.0	1	13	4	4	4	102618	47681	54937	30808	16251	14557	121369	59138	62231	26087	7410	18677	14080	7457	6623	14892	7839	7053	34341	18094	16247	19906	9676	10230	27123	14340	12783	1681.0	173.0	607.0	19.0	245.0	116.0	431.0	1.0	84.0	5.0	1680.0	34.0	299.0	439.0	109.0	799.0	45	1.258957	0.735908	8.830901	0.735908	0.078502	0.746962	2.048487	0.061485	1.205404	0.967696	0.781053	10.56540	4.812102	57.745220	39.0	4.850748	6.274963	39	0.114134	1.369603	0.248151	6.374826	19.651101	22.790985	24.079707	24.779082	4.152246	2	5.706484	38	0.490549	27.553486	32	8.424959	9	22.624362	16.224083	6.620081	4.121874	8.957780	8.824060	15.483912	5.353674	0.225788	3.673942	11.810839	20.392427	9.131977	3.300120	25.462741	1.613152	17.214870	7.922610	2.414138	4.923614	0.514211	0.746962	0.749142	0.848297	1.917736	19.953413	14.052207	12.228576	0.446324	42.22	0.00	1	1000.0	1500.00	1250.00	0	0	0	...	1	1	0	3	0	20.14	0.70	0	0	0	2	1000.00	1500.00	1250.00	0	0	0	2	0	1	2	0	4	0	15.17	1.18	0	0	0	0	3	1000.00	1500.00	1250.00	0	0	0	3	0	1	2	1	0	5	0	14.69	2.87	0	0	3	73000	12	781.82	1227.27	1004.55	1	2	2	7	0	1	3	1	0	7	0	21.58	4.69	1	37550	8	299166	19	676.47	1088.24	882.35	2	5	4	8	0	0	1	10	1	0	14	1	2015	7	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
1	30475	79.2	30.4	8	17	1	0.0	3	1.0	1.0	2.553630e+07	4001	0.496315	0.007122	275	3062.0	0	264	6974.0	0	0	990.0	0	0	0	1	0	17790	8350	9443	574	297	277	2566	1356	1211	861	244	617	275	143	133	264	136	128	646	336	311	3796	2035	1762	506	261	245	295.0	44.0	1.0	0.0	84.0	7.0	92.0	0.0	0.0	0.0	295.0	0.0	2.0	144.0	73.0	31.0	21	4.230425	3.444625	41.335498	3.444625	1.192193	1.332570	4.400427	0.000000	0.742377	16.049420	2.244906	18.50054	5.458057	65.496687	24.0	5.458057	6.859956	24	0.826083	9.912993	0.799853	6.847813	16.975793	19.692960	20.864427	21.722620	2.148398	13	4.410488	27	2.342346	27.421853	50	16.913175	8	29.425443	17.080113	8.545593	4.932827	10.039833	3.654955	15.092542	8.156185	1.313180	4.244082	4.438242	13.445121	8.332180	7.095895	26.807204	3.775300	12.440198	9.672779	1.764298	3.764819	1.694967	1.332570	1.672126	1.162371	12.239901	13.006107	9.661063	4.323941	0.705873	86.33	0.00	0	700.0	1166.67	927.78	0	0	0	...	0	0	0	0	0	59.28	5.31	0	0	0	3	833.33	1500.00	1166.67	0	0	2	0	1	0	1	0	1	0	49.26	4.06	0	0	1	5000	7	757.14	1285.71	1021.43	0	1	3	2	1	1	2	0	0	1	0	39.50	3.32	0	0	2	22000	10	680.00	1200.00	940.00	0	1	6	2	1	1	5	0	0	7	0	39.10	7.70	2	177300	6	231300	20	733.33	1250.00	991.67	2	4	8	4	1	1	2	11	0	1	12	1	2015	7	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
2	30476	40.5	25.1	3	5	2	1960.0	2	4.8	2.0	9.946335e+06	139322	0.065409	0.225825	6400	2232.0	7	6558	7966.0	7	0	1548.0	3	13	0	2	7	36154	16222	19932	13799	6937	6862	91795	44734	47061	33728	9653	24075	6400	3209	3191	6558	3317	3241	15514	7813	7701	8137	3787	4350	12162	6117	6045	561.0	111.0	0.0	0.0	254.0	3.0	189.0	0.0	4.0	0.0	561.0	0.0	5.0	437.0	79.0	40.0	44	1.585306	1.122214	13.466563	1.122214	0.065324	0.194608	2.513006	0.580638	0.900408	11.749900	3.389848	10.19563	3.628293	43.539514	68.0	3.977659	5.375048	59	0.116686	1.400229	1.384824	3.499380	5.627481	8.090528	8.671086	10.320728	0.580638	10	3.499380	1	2.220941	10.093318	5	7.921607	3	1.823381	14.431252	0.826743	2.388288	3.760642	3.290966	16.304596	0.644830	0.966254	1.332737	3.131143	1.464174	1.499581	0.487817	6.718082	0.711768	4.862872	3.506298	1.456661	1.223804	2.330995	0.194608	1.400094	1.177527	9.938735	2.983875	1.988346	0.794245	0.320864	0.00	0.00	3	400.0	750.00	575.00	1	1	1	...	0	0	1	6	2	30.97	8.75	2	34100	1	19	655.56	1111.11	883.33	1	6	6	4	2	1	1	1	12	3	40.90	10.51	6	80237	3	14090	28	633.33	1092.59	862.96	1	7	12	6	2	2	2	0	4	14	4	45.86	9.08	8	215237	6	39106	37	608.33	1069.44	838.89	1	8	19	7	2	2	3	0	5	22	4	25.62	13.59	27	427889	26	1024431	179	668.97	1132.18	900.57	5	53	64	42	11	4	10	21	0	10	71	11	2015	7	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
3	30477	62.8	36.0	17	17	1	2016.0	2	62.8	3.0	2.149409e+07	7122	0.262459	0.017647	489	3062.0	0	469	6974.0	0	0	990.0	0	0	2	0	0	9553	4529	5024	1021	529	493	4568	2414	2155	1533	435	1099	489	254	236	469	242	228	1150	597	553	2155	1206	950	900	465	435	295.0	44.0	1.0	0.0	84.0	7.0	92.0	0.0	0.0	0.0	295.0	0.0	2.0	144.0	73.0	31.0	45	7.931398	6.038848	68.559794	5.713316	3.189083	3.540105	5.612835	0.025446	0.466738	5.061917	2.701804	14.62944	10.284167	123.410001	39.0	10.609698	13.517419	39	3.093209	37.118504	0.233017	8.928836	22.094252	25.062928	26.226045	26.960463	2.722667	38	8.601110	2	4.476081	37.436772	50	13.979650	8	26.895118	19.942295	9.434351	6.218331	13.345715	10.480798	10.723870	11.112624	4.480234	8.577223	15.200509	18.560234	12.253021	6.831966	30.366022	5.731266	15.382678	11.306566	6.589381	8.102094	0.403429	3.540105	5.411312	0.213853	6.153091	18.121220	16.938290	14.171229	0.454087	22.01	0.15	1	300.0	500.00	400.00	0	1	0	...	0	2	0	0	0	28.82	3.59	0	0	0	1	300.00	500.00	400.00	0	1	0	0	0	0	3	0	0	0	31.35	2.99	0	0	0	0	1	300.00	500.00	400.00	0	1	0	0	0	0	3	0	0	0	0	34.87	1.34	0	0	0	0	1	300.00	500.00	400.00	0	1	0	0	0	0	4	0	0	0	0	24.25	1.66	0	0	0	0	5	1560.00	2500.00	2030.00	0	1	0	1	1	2	0	10	0	0	2	0	2015	7	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0
4	30478	40.0	40.0	17	17	1	0.0	1	1.0	1.0	2.553630e+07	4001	0.496315	0.007122	275	3062.0	0	264	6974.0	0	0	990.0	0	0	0	1	0	17790	8350	9443	574	297	277	2566	1356	1211	861	244	617	275	143	133	264	136	128	646	336	311	3796	2035	1762	506	261	245	295.0	44.0	1.0	0.0	84.0	7.0	92.0	0.0	0.0	0.0	295.0	0.0	2.0	144.0	73.0	31.0	21	2.152792	1.722233	20.666800	1.722233	0.897889	1.234235	4.566595	0.427248	0.353642	16.784630	2.250137	19.14919	3.735666	44.827989	24.0	3.735666	4.782323	24	0.630014	7.560163	0.394422	7.123215	17.148737	19.868997	21.038561	21.905792	2.808077	13	3.688405	27	1.727223	25.699461	50	17.366661	8	29.968660	17.397666	9.036942	5.506770	10.102328	3.729416	15.546028	6.433794	1.519553	2.521691	2.715850	13.898607	8.355285	7.401423	25.084813	2.052908	12.893684	9.479093	1.806570	4.338453	1.339078	1.234235	1.192543	1.186621	12.652956	13.459593	9.890758	4.555385	0.066503	3.33	3.70	2	1000.0	1750.00	1375.00	0	0	1	...	0	0	0	0	0	43.85	1.55	0	0	0	3	833.33	1500.00	1166.67	0	0	2	0	1	1	1	0	0	0	38.61	3.12	0	0	2	22000	7	757.14	1285.71	1021.43	0	1	3	2	1	1	2	0	0	3	0	41.64	2.11	0	0	2	22000	9	700.00	1222.22	961.11	0	1	5	2	1	1	4	0	0	6	0	35.62	6.96	1	117300	4	201300	20	747.37	1263.16	1005.26	1	4	8	5	1	1	2	12	0	1	11	1	2015	7	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0

	id	full_sq	life_sq	floor	max_floor	material	build_year	num_room	kitch_sq	state	product_type	sub_area	area_m	raion_popul	green_zone_part	indust_part	children_preschool	preschool_quota	preschool_education_centers_raion	children_school	school_quota	school_education_centers_raion	hospital_beds_raion	healthcare_centers_raion	university_top_20_raion	sport_objects_raion	additional_education_raion	culture_objects_top_25	culture_objects_top_25_raion	shopping_centers_raion	office_raion	incineration_raion	oil_chemistry_raion	radiation_raion	railroad_terminal_raion	big_market_raion	full_all	male_f	female_f	young_all	young_male	young_female	work_all	work_male	work_female	ekder_all	ekder_male	ekder_female	0_6_all	0_6_male	0_6_female	7_14_all	7_14_male	7_14_female	0_17_all	0_17_male	0_17_female	16_29_all	16_29_male	16_29_female	0_13_all	0_13_male	0_13_female	raion_build_count_with_material_info	build_count_block	build_count_wood	build_count_frame	build_count_brick	build_count_monolith	build_count_panel	build_count_slag	build_count_mix	raion_build_count_with_builddate_info	build_count_before_1920	build_count_1921-1945	build_count_1946-1970	build_count_1971-1995	build_count_after_1995	ID_metro	metro_min_avto	metro_km_avto	metro_min_walk	metro_km_walk	kindergarten_km	school_km	park_km	green_zone_km	industrial_km	water_treatment_km	cemetery_km	incineration_km	railroad_station_walk_km	railroad_station_walk_min	ID_railroad_station_walk	railroad_station_avto_km	railroad_station_avto_min	ID_railroad_station_avto	public_transport_station_km	public_transport_station_min_walk	water_km	water_1line	mkad_km	ttk_km	sadovoe_km	bulvar_ring_km	kremlin_km	big_road1_km	ID_big_road1	big_road2_km	ID_big_road2	railroad_km	railroad_1line	zd_vokzaly_avto_km	ID_railroad_terminal	bus_terminal_avto_km	ID_bus_terminal	oil_chemistry_km	nuclear_reactor_km	radiation_km	power_transmission_line_km	thermal_power_plant_km	ts_km	big_market_km	market_shop_km	fitness_km	swim_pool_km	ice_rink_km	stadium_km	basketball_km	hospice_morgue_km	detention_facility_km	public_healthcare_km	university_km	workplaces_km	shopping_centers_km	office_km	additional_education_km	preschool_km	big_church_km	church_synagogue_km	mosque_km	theater_km	museum_km	exhibition_km	...	leisure_count_1000	sport_count_1000	market_count_1000	green_part_1500	prom_part_1500	office_count_1500	office_sqm_1500	trc_count_1500	trc_sqm_1500	cafe_count_1500	cafe_sum_1500_min_price_avg	cafe_sum_1500_max_price_avg	cafe_avg_price_1500	cafe_count_1500_na_price	cafe_count_1500_price_500	cafe_count_1500_price_1000	cafe_count_1500_price_1500	cafe_count_1500_price_2500	cafe_count_1500_price_4000	cafe_count_1500_price_high	big_church_count_1500	church_count_1500	leisure_count_1500	sport_count_1500	market_count_1500	green_part_2000	prom_part_2000	office_count_2000	office_sqm_2000	trc_count_2000	trc_sqm_2000	cafe_count_2000	cafe_sum_2000_min_price_avg	cafe_sum_2000_max_price_avg	cafe_avg_price_2000	cafe_count_2000_na_price	cafe_count_2000_price_500	cafe_count_2000_price_1000	cafe_count_2000_price_1500	cafe_count_2000_price_2500	cafe_count_2000_price_4000	cafe_count_2000_price_high	big_church_count_2000	church_count_2000	leisure_count_2000	sport_count_2000	market_count_2000	green_part_3000	prom_part_3000	office_count_3000	office_sqm_3000	trc_count_3000	trc_sqm_3000	cafe_count_3000	cafe_sum_3000_min_price_avg	cafe_sum_3000_max_price_avg	cafe_avg_price_3000	cafe_count_3000_na_price	cafe_count_3000_price_500	cafe_count_3000_price_1000	cafe_count_3000_price_1500	cafe_count_3000_price_2500	cafe_count_3000_price_4000	cafe_count_3000_price_high	big_church_count_3000	church_count_3000	mosque_count_3000	leisure_count_3000	sport_count_3000	market_count_3000	green_part_5000	prom_part_5000	office_count_5000	office_sqm_5000	trc_count_5000	trc_sqm_5000	cafe_count_5000	cafe_sum_5000_min_price_avg	cafe_sum_5000_max_price_avg	cafe_avg_price_5000	cafe_count_5000_na_price	cafe_count_5000_price_500	cafe_count_5000_price_1000	cafe_count_5000_price_1500	cafe_count_5000_price_2500	cafe_count_5000_price_4000	cafe_count_5000_price_high	big_church_count_5000	church_count_5000	mosque_count_5000	leisure_count_5000	sport_count_5000	market_count_5000	price_doc	year	month	day	max_floornan	materialnan	build_yearnan	num_roomnan	kitch_sqnan	statenan	hospital_beds_raionnan	cafe_sum_500_min_price_avgnan	cafe_sum_500_max_price_avgnan	cafe_avg_price_500nan
0	1	43	27.0	4.0	12.0	1.0	1979.0	2.0	6.0	2.0	1	16	6.407578e+06	155572	0.189727	0.000070	9576	5001.0	5	10309	11065.0	5	240.0	1	0	7	3	0	0	16	1	1	1	0	0	1	86206	40477	45729	21154	11007	10147	98207	52277	45930	36211	10580	25631	9576	4899	4677	10309	5463	4846	23603	12286	11317	17508	9425	8083	18654	9709	8945	211.0	25.0	0.0	0.0	0.0	2.0	184.0	0.0	0.0	211.0	0.0	0.0	0.0	206.0	5.0	1	2.590241	1.131260	13.575119	1.131260	0.145700	0.177975	2.158587	0.600973	1.080934	23.683460	1.804127	3.633334	5.419893	65.038716	1.0	5.419893	6.905893	1	0.274985	3.299822	0.992631	1	1.422391	10.918587	13.100618	13.675657	15.156211	1.422391	1	3.830951	5	1.305159	1	14.231961	101	24.292406	1	18.152338	5.718519	1.210027	1.062513	5.814135	4.308127	10.814172	1.676258	0.485841	3.065047	1.107594	8.148591	3.516513	2.392353	4.248036	0.974743	6.715026	0.884350	0.648488	0.637189	0.947962	0.177975	0.625783	0.628187	3.932040	14.053047	7.389498	7.023705	...	0	6	1	14.27	6.92	3	39554	9	171420	34	566.67	969.70	768.18	1	14	11	6	2	0	0	1	2	0	7	1	11.77	15.97	9	188854	19	1244891	36	614.29	1042.86	828.57	1	15	11	6	2	1	0	1	2	0	10	1	11.98	13.55	12	251554	23	1419204	68	639.68	1079.37	859.52	5	21	22	16	3	1	0	2	4	0	0	21	1	13.09	13.31	29	807385	52	4036616	152	708.57	1185.71	947.14	12	39	48	40	9	4	0	13	22	1	0	52	4	5850000	2011	8	20	1	1	1	1	1	1	0	1	1	1
1	2	34	19.0	3.0	12.0	1.0	1979.0	2.0	6.0	2.0	1	17	9.589337e+06	115352	0.372602	0.049637	6880	3119.0	5	7759	6237.0	8	229.0	1	0	6	1	1	1	3	0	1	1	0	0	1	76284	34200	42084	15727	7925	7802	70194	35622	34572	29431	9266	20165	6880	3466	3414	7759	3909	3850	17700	8998	8702	15164	7571	7593	13729	6929	6800	245.0	83.0	1.0	0.0	67.0	4.0	90.0	0.0	0.0	244.0	1.0	1.0	143.0	84.0	15.0	2	0.936700	0.647337	7.620630	0.635053	0.147754	0.273345	0.550690	0.065321	0.966479	1.317476	4.655004	8.648587	3.411993	40.943917	2.0	3.641773	4.679745	2	0.065263	0.783160	0.698081	1	9.503405	3.103996	6.444333	8.132640	8.698054	2.887377	2	3.103996	4	0.694536	1	9.242586	32	5.706113	2	9.034642	3.489954	2.724295	1.246149	3.419574	0.725560	6.910568	3.424716	0.668364	2.000154	8.972823	6.127073	1.161579	2.543747	12.649879	1.477723	1.852560	0.686252	0.519311	0.688796	1.072315	0.273345	0.967821	0.471447	4.841544	6.829889	0.709260	2.358840	...	4	2	0	21.53	7.71	3	102910	7	127065	17	694.12	1205.88	950.00	0	6	7	1	2	1	0	1	5	4	9	0	22.37	19.25	4	165510	8	179065	21	695.24	1190.48	942.86	0	7	8	3	2	1	0	1	5	4	11	0	18.07	27.32	12	821986	14	491565	30	631.03	1086.21	858.62	1	11	11	4	2	1	0	1	7	0	6	19	1	10.26	27.47	66	2690465	40	2034942	177	673.81	1148.81	911.31	9	49	65	36	15	3	0	15	29	1	10	66	14	6000000	2011	8	23	1	1	1	1	1	1	0	0	0	0
2	3	43	29.0	2.0	12.0	1.0	1979.0	2.0	6.0	2.0	1	16	4.808270e+06	101708	0.112560	0.118537	5879	1463.0	4	6207	5580.0	7	1183.0	1	0	5	1	0	0	0	1	1	1	1	0	1	101982	46076	55906	13028	6835	6193	63388	31813	31575	25292	7609	17683	5879	3095	2784	6207	3269	2938	14884	7821	7063	19401	9045	10356	11252	5916	5336	330.0	59.0	0.0	0.0	206.0	4.0	60.0	1.0	0.0	330.0	1.0	0.0	246.0	63.0	20.0	3	2.120999	1.637996	17.351515	1.445960	0.049102	0.158072	0.374848	0.453172	0.939275	4.912660	3.381083	11.996480	1.277658	15.331896	3.0	1.277658	1.701420	3	0.328756	3.945073	0.468265	1	5.604800	2.927487	6.963403	8.054252	9.067885	0.647250	3	2.927487	4	0.700691	1	9.540544	5	6.710302	3	5.777394	7.506612	0.772216	1.602183	3.682455	3.562188	5.752368	1.375443	0.733101	1.239304	1.978517	0.767569	1.952771	0.621357	7.682303	0.097144	0.841254	1.510089	1.486533	1.543049	0.391957	0.158072	3.178751	0.755946	7.922152	4.273200	3.156423	4.958214	...	0	5	3	9.92	6.73	0	0	1	2600	14	516.67	916.67	716.67	2	4	6	2	0	0	0	0	4	0	6	5	12.99	12.75	4	100200	7	52550	24	563.64	977.27	770.45	2	8	9	4	1	0	0	0	4	0	8	5	12.14	26.46	8	110856	7	52550	41	697.44	1192.31	944.87	2	9	17	9	3	1	0	0	11	0	0	20	6	13.69	21.58	43	1478160	35	1572990	122	702.68	1196.43	949.55	10	29	45	25	10	3	0	11	27	0	4	67	10	5700000	2011	8	27	1	1	1	1	1	1	0	0	0	0
3	4	89	50.0	9.0	12.0	1.0	1979.0	2.0	6.0	2.0	1	18	1.258354e+07	178473	0.194703	0.069753	13087	6839.0	9	13670	17063.0	10	990.0	1	0	17	6	0	0	11	4	1	1	0	0	1	21155	9828	11327	28563	14680	13883	120381	60040	60341	29529	9083	20446	13087	6645	6442	13670	7126	6544	32063	16513	15550	3292	1450	1842	24934	12782	12152	458.0	9.0	51.0	12.0	124.0	50.0	201.0	9.0	2.0	459.0	13.0	24.0	40.0	130.0	252.0	4	1.489049	0.984537	11.565624	0.963802	0.179441	0.236455	0.078090	0.106125	0.451173	15.623710	2.017080	14.317640	4.291432	51.497190	4.0	3.816045	5.271136	4	0.131597	1.579164	1.200336	1	2.677824	14.606501	17.457198	18.309433	19.487005	2.677824	1	2.780449	17	1.999265	1	17.478380	83	6.734618	1	27.667863	9.522538	6.348716	1.767612	11.178333	0.583025	27.892717	0.811275	0.623484	1.950317	6.483172	7.385521	4.923843	3.549558	8.789894	2.163735	10.903161	0.622272	0.599914	0.934273	0.892674	0.236455	1.031777	1.561505	15.300449	16.990677	16.041521	5.029696	...	0	3	1	28.38	6.57	2	11000	7	89492	23	673.91	1130.43	902.17	0	5	9	8	1	0	0	1	0	0	9	2	32.29	5.73	2	11000	7	89492	25	660.00	1120.00	890.00	0	5	11	8	1	0	0	1	1	0	13	2	20.79	3.57	4	167000	12	205756	32	718.75	1218.75	968.75	0	5	14	10	3	0	0	1	2	0	0	18	3	14.18	3.89	8	244166	22	942180	61	931.58	1552.63	1242.11	4	7	21	15	11	2	1	4	4	0	0	26	3	13100000	2011	9	1	1	1	1	1	1	1	1	0	0	0
4	5	77	77.0	4.0	12.0	1.0	1979.0	2.0	6.0	2.0	1	16	8.398461e+06	108171	0.015234	0.037316	5706	3240.0	7	6748	7770.0	9	562.0	4	2	25	2	0	0	10	93	1	1	1	1	1	28179	13522	14657	13368	7159	6209	68043	34236	33807	26760	8563	18197	5706	2982	2724	6748	3664	3084	15237	8113	7124	5164	2583	2581	11631	6223	5408	746.0	48.0	0.0	0.0	643.0	16.0	35.0	3.0	1.0	746.0	371.0	114.0	146.0	62.0	53.0	5	1.257186	0.876620	8.266305	0.688859	0.247901	0.376838	0.258289	0.236214	0.392871	10.683540	2.936581	11.903910	0.853960	10.247521	5.0	1.595898	2.156284	113	0.071480	0.857764	0.820294	1	11.616653	1.721834	0.046810	0.787593	2.578671	1.721834	4	3.133531	10	0.084113	0	1.595898	113	1.423428	4	6.515857	8.671016	1.638318	3.632640	4.587917	2.609420	9.155057	1.969738	0.220288	2.544696	3.975401	3.610754	0.307915	1.864637	3.779781	1.121703	0.991683	0.892668	0.429052	0.077901	0.810801	0.376838	0.378756	0.121681	2.584370	1.112486	1.800125	1.339652	...	6	7	0	4.12	4.83	93	1195735	9	445900	272	766.80	1272.73	1019.76	19	70	74	72	30	6	1	18	30	10	14	2	4.53	5.02	149	1625130	17	564843	483	765.93	1269.23	1017.58	28	130	129	131	50	14	1	35	61	17	21	3	5.06	8.62	305	3420907	60	2296870	1068	853.03	1410.45	1131.74	63	266	267	262	149	57	4	70	121	1	40	77	5	8.38	10.92	689	8404624	114	3503058	2283	853.88	1411.45	1132.66	143	566	578	552	319	108	17	135	236	2	91	195	14	16331452	2011	9	5	1	1	1	1	1	1	0	0	0	0

	id	full_sq	life_sq	floor	max_floor	material	build_year	num_room	kitch_sq	state	product_type	sub_area	area_m	raion_popul	green_zone_part	indust_part	children_preschool	preschool_quota	preschool_education_centers_raion	children_school	school_quota	school_education_centers_raion	school_education_centers_top_20_raion	hospital_beds_raion	healthcare_centers_raion	sport_objects_raion	additional_education_raion	culture_objects_top_25	shopping_centers_raion	office_raion	thermal_power_plant_raion	incineration_raion	oil_chemistry_raion	radiation_raion	railroad_terminal_raion	big_market_raion	nuclear_reactor_raion	detention_facility_raion	full_all	male_f	female_f	young_all	young_male	young_female	work_all	work_male	work_female	ekder_all	ekder_male	ekder_female	0_6_all	0_6_male	0_6_female	7_14_all	7_14_male	7_14_female	0_17_all	0_17_male	0_17_female	16_29_all	16_29_male	16_29_female	0_13_all	0_13_male	0_13_female	raion_build_count_with_material_info	build_count_block	build_count_wood	build_count_frame	build_count_brick	build_count_monolith	build_count_panel	build_count_foam	build_count_slag	build_count_mix	raion_build_count_with_builddate_info	build_count_before_1920	build_count_1921-1945	build_count_1946-1970	build_count_1971-1995	build_count_after_1995	ID_metro	metro_min_avto	metro_km_avto	metro_min_walk	metro_km_walk	kindergarten_km	school_km	park_km	green_zone_km	industrial_km	water_treatment_km	cemetery_km	incineration_km	railroad_station_walk_km	railroad_station_walk_min	ID_railroad_station_walk	railroad_station_avto_km	railroad_station_avto_min	ID_railroad_station_avto	public_transport_station_km	public_transport_station_min_walk	water_km	water_1line	mkad_km	ttk_km	sadovoe_km	bulvar_ring_km	kremlin_km	big_road1_km	ID_big_road1	big_road1_1line	big_road2_km	ID_big_road2	railroad_km	railroad_1line	zd_vokzaly_avto_km	ID_railroad_terminal	bus_terminal_avto_km	ID_bus_terminal	oil_chemistry_km	nuclear_reactor_km	radiation_km	power_transmission_line_km	thermal_power_plant_km	ts_km	big_market_km	market_shop_km	fitness_km	swim_pool_km	ice_rink_km	stadium_km	basketball_km	hospice_morgue_km	detention_facility_km	public_healthcare_km	university_km	workplaces_km	shopping_centers_km	office_km	additional_education_km	preschool_km	big_church_km	church_synagogue_km	mosque_km	theater_km	museum_km	exhibition_km	...	big_church_count_1000	church_count_1000	leisure_count_1000	sport_count_1000	market_count_1000	green_part_1500	prom_part_1500	office_count_1500	office_sqm_1500	trc_count_1500	cafe_count_1500	cafe_sum_1500_min_price_avg	cafe_sum_1500_max_price_avg	cafe_avg_price_1500	cafe_count_1500_na_price	cafe_count_1500_price_500	cafe_count_1500_price_1000	cafe_count_1500_price_1500	cafe_count_1500_price_2500	big_church_count_1500	church_count_1500	leisure_count_1500	sport_count_1500	market_count_1500	green_part_2000	prom_part_2000	office_count_2000	office_sqm_2000	trc_count_2000	trc_sqm_2000	cafe_count_2000	cafe_sum_2000_min_price_avg	cafe_sum_2000_max_price_avg	cafe_avg_price_2000	cafe_count_2000_na_price	cafe_count_2000_price_500	cafe_count_2000_price_1000	cafe_count_2000_price_1500	cafe_count_2000_price_2500	big_church_count_2000	church_count_2000	mosque_count_2000	leisure_count_2000	sport_count_2000	market_count_2000	green_part_3000	prom_part_3000	office_count_3000	office_sqm_3000	trc_count_3000	trc_sqm_3000	cafe_count_3000	cafe_sum_3000_min_price_avg	cafe_sum_3000_max_price_avg	cafe_avg_price_3000	cafe_count_3000_na_price	cafe_count_3000_price_500	cafe_count_3000_price_1000	cafe_count_3000_price_1500	cafe_count_3000_price_2500	big_church_count_3000	church_count_3000	mosque_count_3000	leisure_count_3000	sport_count_3000	market_count_3000	green_part_5000	prom_part_5000	office_count_5000	office_sqm_5000	trc_count_5000	trc_sqm_5000	cafe_count_5000	cafe_sum_5000_min_price_avg	cafe_sum_5000_max_price_avg	cafe_avg_price_5000	cafe_count_5000_na_price	cafe_count_5000_price_500	cafe_count_5000_price_1000	cafe_count_5000_price_1500	cafe_count_5000_price_2500	cafe_count_5000_price_4000	big_church_count_5000	church_count_5000	mosque_count_5000	leisure_count_5000	sport_count_5000	market_count_5000	year	month	day	life_sqnan	preschool_quotanan	school_quotanan	hospital_beds_raionnan	raion_build_count_with_material_infonan	build_count_blocknan	build_count_woodnan	build_count_framenan	build_count_bricknan	build_count_monolithnan	build_count_panelnan	build_count_foamnan	build_count_slagnan	build_count_mixnan	raion_build_count_with_builddate_infonan	build_count_before_1920nan	build_count_1921-1945nan	build_count_1946-1970nan	build_count_1971-1995nan	build_count_after_1995nan	cafe_sum_500_min_price_avgnan	cafe_sum_500_max_price_avgnan	cafe_avg_price_500nan
0	30474	39.0	20.7	2	9	1	1998.0	1	8.9	3.0	7661	7661	2.615514e+07	178264	0.137846	0.041116	14080	11926.0	11	14892	24750.0	13	1	990.0	1	13	4	7661	4	4	7661	7661	7661	7661	7661	7661	7661	7661	102618	47681	54937	30808	16251	14557	121369	59138	62231	26087	7410	18677	14080	7457	6623	14892	7839	7053	34341	18094	16247	19906	9676	10230	27123	14340	12783	1681.0	173.0	607.0	19.0	245.0	116.0	431.0	1.0	84.0	5.0	1680.0	34.0	299.0	439.0	109.0	799.0	45	1.258957	0.735908	8.830901	0.735908	0.078502	0.746962	2.048487	0.061485	1.205404	0.967696	0.781053	10.56540	4.812102	57.745220	39.0	4.850748	6.274963	39	0.114134	1.369603	0.248151	7661	6.374826	19.651101	22.790985	24.079707	24.779082	4.152246	2	7661	5.706484	38	0.490549	7661	27.553486	32	8.424959	9	22.624362	16.224083	6.620081	4.121874	8.957780	8.824060	15.483912	5.353674	0.225788	3.673942	11.810839	20.392427	9.131977	3.300120	25.462741	1.613152	17.214870	7.922610	2.414138	4.923614	0.514211	0.746962	0.749142	0.848297	1.917736	19.953413	14.052207	12.228576	...	1	1	0	3	0	20.14	0.70	0	0	0	2	1000.00	1500.00	1250.00	0	0	0	2	0	1	2	0	4	0	15.17	1.18	0	0	0	0	3	1000.00	1500.00	1250.00	0	0	0	3	0	1	2	1	0	5	0	14.69	2.87	0	0	3	73000	12	781.82	1227.27	1004.55	1	2	2	7	0	1	3	1	0	7	0	21.58	4.69	1	37550	8	299166	19	676.47	1088.24	882.35	2	5	4	8	0	0	1	10	1	0	14	1	2015	7	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
1	30475	79.2	30.4	8	17	1	0.0	3	1.0	1.0	7661	7661	2.553630e+07	4001	0.496315	0.007122	275	3062.0	0	264	6974.0	0	0	990.0	0	0	0	7661	1	0	7661	7661	7661	7661	7661	7661	7661	7661	17790	8350	9443	574	297	277	2566	1356	1211	861	244	617	275	143	133	264	136	128	646	336	311	3796	2035	1762	506	261	245	295.0	44.0	1.0	0.0	84.0	7.0	92.0	0.0	0.0	0.0	295.0	0.0	2.0	144.0	73.0	31.0	21	4.230425	3.444625	41.335498	3.444625	1.192193	1.332570	4.400427	0.000000	0.742377	16.049420	2.244906	18.50054	5.458057	65.496687	24.0	5.458057	6.859956	24	0.826083	9.912993	0.799853	7661	6.847813	16.975793	19.692960	20.864427	21.722620	2.148398	13	7661	4.410488	27	2.342346	7661	27.421853	50	16.913175	8	29.425443	17.080113	8.545593	4.932827	10.039833	3.654955	15.092542	8.156185	1.313180	4.244082	4.438242	13.445121	8.332180	7.095895	26.807204	3.775300	12.440198	9.672779	1.764298	3.764819	1.694967	1.332570	1.672126	1.162371	12.239901	13.006107	9.661063	4.323941	...	0	0	0	0	0	59.28	5.31	0	0	0	3	833.33	1500.00	1166.67	0	0	2	0	1	0	1	0	1	0	49.26	4.06	0	0	1	5000	7	757.14	1285.71	1021.43	0	1	3	2	1	1	2	0	0	1	0	39.50	3.32	0	0	2	22000	10	680.00	1200.00	940.00	0	1	6	2	1	1	5	0	0	7	0	39.10	7.70	2	177300	6	231300	20	733.33	1250.00	991.67	2	4	8	4	1	1	2	11	0	1	12	1	2015	7	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
2	30476	40.5	25.1	3	5	2	1960.0	2	4.8	2.0	7661	7661	9.946335e+06	139322	0.065409	0.225825	6400	2232.0	7	6558	7966.0	7	0	1548.0	3	13	0	7661	2	7	7661	7661	7661	7661	7661	7661	7661	7661	36154	16222	19932	13799	6937	6862	91795	44734	47061	33728	9653	24075	6400	3209	3191	6558	3317	3241	15514	7813	7701	8137	3787	4350	12162	6117	6045	561.0	111.0	0.0	0.0	254.0	3.0	189.0	0.0	4.0	0.0	561.0	0.0	5.0	437.0	79.0	40.0	44	1.585306	1.122214	13.466563	1.122214	0.065324	0.194608	2.513006	0.580638	0.900408	11.749900	3.389848	10.19563	3.628293	43.539514	68.0	3.977659	5.375048	59	0.116686	1.400229	1.384824	7661	3.499380	5.627481	8.090528	8.671086	10.320728	0.580638	10	7661	3.499380	1	2.220941	7661	10.093318	5	7.921607	3	1.823381	14.431252	0.826743	2.388288	3.760642	3.290966	16.304596	0.644830	0.966254	1.332737	3.131143	1.464174	1.499581	0.487817	6.718082	0.711768	4.862872	3.506298	1.456661	1.223804	2.330995	0.194608	1.400094	1.177527	9.938735	2.983875	1.988346	0.794245	...	0	0	1	6	2	30.97	8.75	2	34100	1	19	655.56	1111.11	883.33	1	6	6	4	2	1	1	1	12	3	40.90	10.51	6	80237	3	14090	28	633.33	1092.59	862.96	1	7	12	6	2	2	2	0	4	14	4	45.86	9.08	8	215237	6	39106	37	608.33	1069.44	838.89	1	8	19	7	2	2	3	0	5	22	4	25.62	13.59	27	427889	26	1024431	179	668.97	1132.18	900.57	5	53	64	42	11	4	10	21	0	10	71	11	2015	7	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
3	30477	62.8	36.0	17	17	1	2016.0	2	62.8	3.0	7661	7661	2.149409e+07	7122	0.262459	0.017647	489	3062.0	0	469	6974.0	0	0	990.0	0	0	2	7661	0	0	7661	7661	7661	7661	7661	7661	7661	7661	9553	4529	5024	1021	529	493	4568	2414	2155	1533	435	1099	489	254	236	469	242	228	1150	597	553	2155	1206	950	900	465	435	295.0	44.0	1.0	0.0	84.0	7.0	92.0	0.0	0.0	0.0	295.0	0.0	2.0	144.0	73.0	31.0	45	7.931398	6.038848	68.559794	5.713316	3.189083	3.540105	5.612835	0.025446	0.466738	5.061917	2.701804	14.62944	10.284167	123.410001	39.0	10.609698	13.517419	39	3.093209	37.118504	0.233017	7661	8.928836	22.094252	25.062928	26.226045	26.960463	2.722667	38	7661	8.601110	2	4.476081	7661	37.436772	50	13.979650	8	26.895118	19.942295	9.434351	6.218331	13.345715	10.480798	10.723870	11.112624	4.480234	8.577223	15.200509	18.560234	12.253021	6.831966	30.366022	5.731266	15.382678	11.306566	6.589381	8.102094	0.403429	3.540105	5.411312	0.213853	6.153091	18.121220	16.938290	14.171229	...	0	2	0	0	0	28.82	3.59	0	0	0	1	300.00	500.00	400.00	0	1	0	0	0	0	3	0	0	0	31.35	2.99	0	0	0	0	1	300.00	500.00	400.00	0	1	0	0	0	0	3	0	0	0	0	34.87	1.34	0	0	0	0	1	300.00	500.00	400.00	0	1	0	0	0	0	4	0	0	0	0	24.25	1.66	0	0	0	0	5	1560.00	2500.00	2030.00	0	1	0	1	1	2	0	10	0	0	2	0	2015	7	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0
4	30478	40.0	40.0	17	17	1	0.0	1	1.0	1.0	7661	7661	2.553630e+07	4001	0.496315	0.007122	275	3062.0	0	264	6974.0	0	0	990.0	0	0	0	7661	1	0	7661	7661	7661	7661	7661	7661	7661	7661	17790	8350	9443	574	297	277	2566	1356	1211	861	244	617	275	143	133	264	136	128	646	336	311	3796	2035	1762	506	261	245	295.0	44.0	1.0	0.0	84.0	7.0	92.0	0.0	0.0	0.0	295.0	0.0	2.0	144.0	73.0	31.0	21	2.152792	1.722233	20.666800	1.722233	0.897889	1.234235	4.566595	0.427248	0.353642	16.784630	2.250137	19.14919	3.735666	44.827989	24.0	3.735666	4.782323	24	0.630014	7.560163	0.394422	7661	7.123215	17.148737	19.868997	21.038561	21.905792	2.808077	13	7661	3.688405	27	1.727223	7661	25.699461	50	17.366661	8	29.968660	17.397666	9.036942	5.506770	10.102328	3.729416	15.546028	6.433794	1.519553	2.521691	2.715850	13.898607	8.355285	7.401423	25.084813	2.052908	12.893684	9.479093	1.806570	4.338453	1.339078	1.234235	1.192543	1.186621	12.652956	13.459593	9.890758	4.555385	...	0	0	0	0	0	43.85	1.55	0	0	0	3	833.33	1500.00	1166.67	0	0	2	0	1	1	1	0	0	0	38.61	3.12	0	0	2	22000	7	757.14	1285.71	1021.43	0	1	3	2	1	1	2	0	0	3	0	41.64	2.11	0	0	2	22000	9	700.00	1222.22	961.11	0	1	5	2	1	1	4	0	0	6	0	35.62	6.96	1	117300	4	201300	20	747.37	1263.16	1005.26	1	4	8	5	1	1	2	12	0	1	11	1	2015	7	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0

	id	full_sq	life_sq	floor	max_floor	material	build_year	num_room	kitch_sq	state	product_type	sub_area	area_m	raion_popul	green_zone_part	indust_part	children_preschool	preschool_quota	preschool_education_centers_raion	children_school	school_quota	school_education_centers_raion	hospital_beds_raion	healthcare_centers_raion	university_top_20_raion	sport_objects_raion	additional_education_raion	culture_objects_top_25	culture_objects_top_25_raion	shopping_centers_raion	office_raion	incineration_raion	oil_chemistry_raion	radiation_raion	railroad_terminal_raion	big_market_raion	full_all	male_f	female_f	young_all	young_male	young_female	work_all	work_male	work_female	ekder_all	ekder_male	ekder_female	0_6_all	0_6_male	0_6_female	7_14_all	7_14_male	7_14_female	0_17_all	0_17_male	0_17_female	16_29_all	16_29_male	16_29_female	0_13_all	0_13_male	0_13_female	raion_build_count_with_material_info	build_count_block	build_count_wood	build_count_frame	build_count_brick	build_count_monolith	build_count_panel	build_count_slag	build_count_mix	raion_build_count_with_builddate_info	build_count_before_1920	build_count_1921-1945	build_count_1946-1970	build_count_1971-1995	build_count_after_1995	ID_metro	metro_min_avto	metro_km_avto	metro_min_walk	metro_km_walk	kindergarten_km	school_km	park_km	green_zone_km	industrial_km	water_treatment_km	cemetery_km	incineration_km	railroad_station_walk_km	railroad_station_walk_min	ID_railroad_station_walk	railroad_station_avto_km	railroad_station_avto_min	ID_railroad_station_avto	public_transport_station_km	public_transport_station_min_walk	water_km	water_1line	mkad_km	ttk_km	sadovoe_km	bulvar_ring_km	kremlin_km	big_road1_km	ID_big_road1	big_road2_km	ID_big_road2	railroad_km	railroad_1line	zd_vokzaly_avto_km	ID_railroad_terminal	bus_terminal_avto_km	ID_bus_terminal	oil_chemistry_km	nuclear_reactor_km	radiation_km	power_transmission_line_km	thermal_power_plant_km	ts_km	big_market_km	market_shop_km	fitness_km	swim_pool_km	ice_rink_km	stadium_km	basketball_km	hospice_morgue_km	detention_facility_km	public_healthcare_km	university_km	workplaces_km	shopping_centers_km	office_km	additional_education_km	preschool_km	big_church_km	church_synagogue_km	mosque_km	theater_km	museum_km	exhibition_km	...	leisure_count_1000	sport_count_1000	market_count_1000	green_part_1500	prom_part_1500	office_count_1500	office_sqm_1500	trc_count_1500	trc_sqm_1500	cafe_count_1500	cafe_sum_1500_min_price_avg	cafe_sum_1500_max_price_avg	cafe_avg_price_1500	cafe_count_1500_na_price	cafe_count_1500_price_500	cafe_count_1500_price_1000	cafe_count_1500_price_1500	cafe_count_1500_price_2500	cafe_count_1500_price_4000	cafe_count_1500_price_high	big_church_count_1500	church_count_1500	leisure_count_1500	sport_count_1500	market_count_1500	green_part_2000	prom_part_2000	office_count_2000	office_sqm_2000	trc_count_2000	trc_sqm_2000	cafe_count_2000	cafe_sum_2000_min_price_avg	cafe_sum_2000_max_price_avg	cafe_avg_price_2000	cafe_count_2000_na_price	cafe_count_2000_price_500	cafe_count_2000_price_1000	cafe_count_2000_price_1500	cafe_count_2000_price_2500	cafe_count_2000_price_4000	cafe_count_2000_price_high	big_church_count_2000	church_count_2000	leisure_count_2000	sport_count_2000	market_count_2000	green_part_3000	prom_part_3000	office_count_3000	office_sqm_3000	trc_count_3000	trc_sqm_3000	cafe_count_3000	cafe_sum_3000_min_price_avg	cafe_sum_3000_max_price_avg	cafe_avg_price_3000	cafe_count_3000_na_price	cafe_count_3000_price_500	cafe_count_3000_price_1000	cafe_count_3000_price_1500	cafe_count_3000_price_2500	cafe_count_3000_price_4000	cafe_count_3000_price_high	big_church_count_3000	church_count_3000	mosque_count_3000	leisure_count_3000	sport_count_3000	market_count_3000	green_part_5000	prom_part_5000	office_count_5000	office_sqm_5000	trc_count_5000	trc_sqm_5000	cafe_count_5000	cafe_sum_5000_min_price_avg	cafe_sum_5000_max_price_avg	cafe_avg_price_5000	cafe_count_5000_na_price	cafe_count_5000_price_500	cafe_count_5000_price_1000	cafe_count_5000_price_1500	cafe_count_5000_price_2500	cafe_count_5000_price_4000	cafe_count_5000_price_high	big_church_count_5000	church_count_5000	mosque_count_5000	leisure_count_5000	sport_count_5000	market_count_5000	price_doc	year	month	day	max_floornan	materialnan	build_yearnan	num_roomnan	kitch_sqnan	statenan	hospital_beds_raionnan	cafe_sum_500_min_price_avgnan	cafe_sum_500_max_price_avgnan	cafe_avg_price_500nan
0	0.000000	1.324738	1.192660	0.326634	0.910235	1.0	2.026877	2.0	0.583198	2.0	1	16	2.751939	2.481138	NaN	NaN	2.215612	2.142110	5	2.223625	2.231255	5	1.701222	1	0	7	3	0	0	16	-inf	1	1	0	0	1	2.430494	2.361655	2.373089	2.298535	2.230690	2.221910	2.441898	2.385483	2.373498	2.351101	2.226430	2.317627	2.215612	2.139688	2.134215	2.223625	2.152431	2.138407	2.309474	2.242433	2.233670	2.279359	2.213876	2.196949	2.285827	2.217115	2.208148	1.677444	1.169032	NaN	0.0	NaN	-0.366513	1.651527	0.0	0.0	5.351858	NaN	NaN	NaN	1.672953	0.475885	-inf	-0.049452	-2.092876	0.958675	-2.092876	NaN	NaN	-0.262074	NaN	-2.553283	1.152083	-0.527503	0.254759	0.524774	1.429110	-inf	0.524774	0.658750	-inf	NaN	0.177199	NaN	1	-1.043160	0.871489	0.944940	0.961500	1.000047	-1.043160	-inf	0.294990	0.475885	-1.323037	1	0.976629	101	1.160072	1	1.064297	0.556015	-1.657353	-2.802852	0.565480	0.378781	0.867461	-0.660556	NaN	0.113385	-2.280919	0.740911	0.229102	-0.136648	0.369117	NaN	0.644140	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.314196	0.971854	0.693177	0.667466	...	-inf	6	1	0.977634	0.659805	0.094048	2.359478	2.197225	2.489220	1.260266	1.846844	1.928181	1.893718	-inf	0.970422	0.874591	0.583198	-0.366513	NaN	0	-inf	-0.366513	NaN	0.665730	1	0.902416	1.019104	0.787195	2.497225	1.079918	2.641523	1.276345	1.859491	1.938702	1.905044	-inf	0.996229	0.874591	0.583198	-0.366513	-inf	0	-inf	-0.366513	NaN	0.834032	1	0.909564	0.957965	0.910235	2.520548	1.142787	2.650817	1.439718	1.865779	1.943641	1.910486	0.475885	1.113344	1.128508	1.019781	0.094048	-inf	0	-0.366513	0.326634	0	NaN	1.113344	1	0.944625	0.951085	1.214110	2.610184	1.374030	2.722013	1.614203	1.881486	1.957005	1.924752	0.910235	1.298436	1.353565	1.305323	0.787195	0.326634	NaN	0.941939	1.128508	1	NaN	1.374030	4	15.581952	2011	8	2.995732	1	1	1	1	1	1	0	1	1	1
1	0.693147	1.260266	1.079918	0.094048	0.910235	1.0	2.026877	2.0	0.583198	2.0	1	17	2.777338	2.455799	NaN	NaN	2.178877	2.085084	5	2.192392	2.167710	8	1.692624	1	0	6	1	1	1	3	NaN	1	1	0	0	1	2.419676	2.345643	2.365318	2.268318	2.194752	2.193009	2.412248	2.349537	2.346678	2.331153	2.212015	2.293716	2.178877	2.098111	2.096254	2.192392	2.112760	2.110919	2.280474	2.208797	2.205116	2.264538	2.189649	2.189974	2.254158	2.179679	2.177552	1.704977	1.485877	-inf	0.0	1.436201	0.326634	1.504035	0.0	0.0	5.497168	-inf	-inf	1.601979	1.488584	0.996229	-0.366513	NaN	NaN	0.708459	NaN	NaN	NaN	NaN	NaN	NaN	-1.288377	0.430446	0.768902	0.204814	1.311626	-0.366513	0.256556	0.433886	-0.366513	NaN	NaN	NaN	1	0.811663	0.124596	0.622296	0.739976	0.771542	0.058597	-0.366513	0.124596	0.326634	NaN	1	0.799227	32	0.554769	2	0.788942	0.223054	0.002207	-1.513865	0.206621	NaN	0.659100	0.207842	NaN	-0.366402	0.785818	0.594827	-1.898586	-0.068666	0.931238	-0.940322	-0.483586	NaN	NaN	NaN	-2.661836	NaN	NaN	NaN	0.455672	0.653006	NaN	-0.152953	...	1.386294	2	0	1.121498	0.714183	0.094048	2.445959	1.945910	2.464062	1.041412	1.878341	1.959385	1.925192	NaN	0.583198	0.665730	-inf	-0.366513	-inf	0	-inf	0.475885	0.326634	0.787195	0	1.133890	1.084348	0.326634	2.486305	0.732099	2.492834	1.113344	1.878588	1.957572	1.924091	NaN	0.665730	0.732099	0.094048	-0.366513	-inf	0	-inf	0.475885	0.326634	0.874591	0	1.062727	1.196229	0.910235	2.611501	0.970422	2.573021	1.224128	1.863670	1.944545	1.910331	-inf	0.874591	0.874591	0.326634	-0.366513	-inf	0	-inf	0.665730	0	0.583198	1.079918	1	0.845118	1.197883	1.432618	2.694980	1.305323	2.675939	1.644061	1.873792	1.952528	1.919109	0.787195	1.358877	1.428968	1.276345	0.996229	0.094048	NaN	0.996229	1.214110	1	0.834032	1.432618	14	15.607270	2011	8	3.135494	1	1	1	1	1	1	0	0	0	0
2	1.098612	1.324738	1.214110	-0.366513	0.910235	1.0	2.026877	2.0	0.583198	2.0	1	16	2.733448	2.444940	NaN	NaN	2.160923	1.986263	4	2.167159	2.154890	7	1.956682	1	0	5	1	0	0	0	-inf	1	1	1	0	1	2.445174	2.373793	2.391642	2.248642	2.178134	2.166900	2.403066	2.338688	2.337964	2.316315	2.190210	2.280376	2.160923	2.084123	2.070860	2.167159	2.090906	2.077625	2.262600	2.193280	2.181843	2.289812	2.209369	2.224118	2.233053	2.161645	2.149694	1.757701	1.405493	NaN	0.0	1.672953	0.326634	1.409607	1.0	0.0	5.799093	-inf	NaN	1.705717	1.421453	1.097189	0.094048	-0.285169	-0.706286	1.048609	-0.997573	NaN	NaN	NaN	NaN	NaN	0.464875	0.197371	0.910117	-1.406380	1.004278	0.094048	-1.406380	-0.632122	0.094048	NaN	0.316610	NaN	1	0.544429	0.071524	0.663032	0.735344	0.790609	NaN	0.094048	0.071524	0.326634	NaN	1	0.813394	5	0.643770	3	0.561872	0.701008	NaN	-0.752118	0.265114	0.239312	0.559394	-1.143268	NaN	-1.539213	-0.382216	NaN	-0.401599	NaN	0.712420	NaN	NaN	-0.886323	-0.925214	-0.835263	NaN	NaN	0.145388	NaN	0.727386	0.373192	0.139274	0.470657	...	-inf	5	3	0.830538	0.645309	NaN	NaN	0.000000	2.062202	0.970422	1.832166	1.919969	1.883216	-0.366513	0.326634	0.583198	-0.366513	NaN	NaN	0	NaN	0.326634	NaN	0.583198	5	0.941639	0.934339	0.326634	2.443644	0.665730	2.385963	1.156269	1.845998	1.929311	1.894162	-0.366513	0.732099	0.787195	0.326634	-inf	NaN	0	NaN	0.326634	NaN	0.732099	5	0.914892	1.186511	0.732099	2.452382	0.665730	2.385963	1.311994	1.879071	1.957789	1.924402	-0.366513	0.787195	1.041412	0.787195	0.094048	-inf	0	NaN	0.874591	0	NaN	1.097189	6	0.961901	1.122253	1.324738	2.653686	1.268453	2.658054	1.569453	1.880213	1.958276	1.925122	0.834032	1.214110	1.336753	1.169032	0.834032	0.094048	NaN	0.874591	1.192660	0	0.326634	1.436201	10	15.555977	2011	8	3.295837	1	1	1	1	1	1	0	0	0	0
3	1.386294	1.501549	1.364055	0.787195	0.910235	1.0	2.026877	2.0	0.583198	2.0	1	18	2.794099	2.492560	NaN	NaN	2.249118	2.178200	9	2.253706	2.276720	10	1.931189	1	0	17	6	0	0	11	0.326634	1	1	0	0	1	2.298540	2.218441	2.233765	2.328240	2.261163	2.255328	2.459454	2.398147	2.398601	2.331476	2.209829	2.295111	2.249118	2.174936	2.171405	2.253706	2.182844	2.173194	2.339443	2.273352	2.267146	2.091772	1.985037	2.017381	2.314908	2.246628	2.241268	1.812684	0.787195	1.369104	12.0	1.572832	1.364055	1.668330	9.0	2.0	6.129050	0.941939	1.156269	1.305323	1.582588	1.710085	0.326634	-0.920957	NaN	0.895287	NaN	NaN	NaN	NaN	NaN	NaN	1.011161	-0.354319	0.978887	0.376119	1.371568	0.326634	0.292083	0.508170	0.326634	NaN	-0.783301	-1.700447	1	-0.015109	0.986364	1.050735	1.067265	1.088477	-0.015109	-inf	0.022361	1.041412	-0.367043	1	1.051159	83	0.645668	1	1.200047	0.812556	0.614241	-0.562769	0.881276	NaN	1.202481	NaN	NaN	-0.403479	0.625516	0.692908	0.466303	0.236512	0.776386	-0.258983	0.870897	NaN	NaN	NaN	NaN	NaN	-3.464701	-0.808221	1.003526	1.041218	1.020716	0.479558	...	-inf	3	1	1.207671	0.632608	-0.366513	2.230622	1.945910	2.433780	1.142787	1.873815	1.950237	1.917629	NaN	0.475885	0.787195	0.732099	-inf	NaN	0	-inf	NaN	NaN	0.787195	2	1.245525	0.557165	-0.366513	2.230622	0.665730	2.433780	1.169032	1.870608	1.948918	1.915631	NaN	0.475885	0.874591	0.732099	-inf	NaN	0	-inf	-inf	NaN	0.941939	2	1.110037	0.241035	0.326634	2.487050	0.910235	2.504255	1.242925	1.883657	1.960881	1.928038	NaN	0.475885	0.970422	0.834032	0.094048	NaN	0	-inf	-0.366513	0	NaN	1.061385	3	0.975251	0.306314	0.732099	2.518148	1.128508	2.621472	1.413636	1.922332	1.994388	1.963549	0.326634	0.665730	1.113344	0.996229	0.874591	-0.366513	-inf	0.326634	0.326634	0	NaN	1.181143	3	16.388123	2011	9	0.000000	1	1	1	1	1	1	1	0	0	0
4	1.609438	1.468751	1.468751	0.326634	0.910235	1.0	2.026877	2.0	0.583198	2.0	1	16	2.769055	2.450269	NaN	NaN	2.157475	2.089804	7	2.176682	2.192550	9	1.845537	4	2	25	2	0	0	10	1.511296	1	1	1	1	1	2.326920	2.252562	2.261000	2.251357	2.183365	2.167195	2.409455	2.345744	2.344535	2.321864	2.203340	2.283302	2.157475	2.079485	2.068110	2.176682	2.104903	2.083680	2.265037	2.197360	2.182813	2.145869	2.061368	2.061269	2.236598	2.167453	2.151254	1.889298	1.353565	NaN	0.0	1.866580	1.019781	1.268453	3.0	1.0	6.614726	1.777695	1.555235	1.606154	1.417583	1.378840	0.475885	-1.474574	NaN	0.747724	NaN	NaN	NaN	NaN	NaN	NaN	0.862343	0.074408	0.906994	NaN	0.844595	0.475885	-0.760491	-0.263463	1.553373	NaN	NaN	NaN	1	0.897083	-0.609928	NaN	NaN	-0.054167	-0.609928	0.326634	0.132922	0.834032	NaN	0	-0.760491	113	-1.041095	4	0.628203	0.770102	-0.705888	0.254611	0.420962	-0.041731	0.794939	-0.388755	NaN	-0.068267	0.322175	0.249915	NaN	-0.473102	0.284928	-2.164146	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-0.051839	-2.238699	-0.531273	-1.229598	...	1.791759	7	0	0.347732	0.454158	1.511296	2.638648	2.197225	2.565553	1.723802	1.893447	1.966961	1.935473	1.079918	1.446565	1.459560	1.453174	1.224128	0.583198	1	1.061385	1.224128	0.834032	0.970422	2	0.412588	0.478362	1.610227	2.660336	1.041412	2.583568	1.821321	1.893276	1.966576	1.935164	1.203634	1.582588	1.581000	1.584161	1.364055	0.970422	1	1.268453	1.413636	1.041412	1.113344	3	0.483269	0.767366	1.744023	2.711073	1.409607	2.684239	1.942123	1.909364	1.981231	1.950402	1.421453	1.719815	1.720487	1.717098	1.610227	1.397	4	1.446565	1.567739	1	1.305323	1.468751	5	0.754171	0.871543	1.877209	2.769101	1.555235	2.712649	2.045529	1.909511	1.981329	1.950517	1.601979	1.846657	1.849961	1.842698	1.751838	1.543753	1.041412	1.590311	1.698150	2	1.506488	1.662599	14	16.608603	2011	9	1.609438	1	1	1	1	1	1	0	0	0	0

	id	full_sq	life_sq	floor	max_floor	material	build_year	num_room	kitch_sq	state	area_m	raion_popul	green_zone_part	indust_part	children_preschool	preschool_quota	preschool_education_centers_raion	children_school	school_quota	school_education_centers_raion	school_education_centers_top_20_raion	hospital_beds_raion	healthcare_centers_raion	university_top_20_raion	sport_objects_raion	additional_education_raion	culture_objects_top_25_raion	shopping_centers_raion	office_raion	full_all	male_f	female_f	young_all	young_male	young_female	work_all	work_male	work_female	ekder_all	ekder_male	ekder_female	0_6_all	0_6_male	0_6_female	7_14_all	7_14_male	7_14_female	0_17_all	0_17_male	0_17_female	16_29_all	16_29_male	16_29_female	0_13_all	0_13_male	0_13_female	raion_build_count_with_material_info	build_count_block	build_count_wood	build_count_frame	build_count_brick	build_count_monolith	build_count_panel	build_count_foam	build_count_slag	build_count_mix	raion_build_count_with_builddate_info	build_count_before_1920	build_count_1921-1945	build_count_1946-1970	build_count_1971-1995	build_count_after_1995	ID_metro	metro_min_avto	metro_km_avto	metro_min_walk	metro_km_walk	kindergarten_km	school_km	park_km	green_zone_km	industrial_km	water_treatment_km	cemetery_km	incineration_km	railroad_station_walk_km	railroad_station_walk_min	ID_railroad_station_walk	railroad_station_avto_km	railroad_station_avto_min	ID_railroad_station_avto	public_transport_station_km	public_transport_station_min_walk	water_km	mkad_km	ttk_km	sadovoe_km	bulvar_ring_km	kremlin_km	big_road1_km	ID_big_road1	big_road2_km	ID_big_road2	railroad_km	zd_vokzaly_avto_km	ID_railroad_terminal	bus_terminal_avto_km	ID_bus_terminal	oil_chemistry_km	nuclear_reactor_km	radiation_km	power_transmission_line_km	thermal_power_plant_km	ts_km	big_market_km	market_shop_km	fitness_km	swim_pool_km	ice_rink_km	stadium_km	basketball_km	hospice_morgue_km	detention_facility_km	public_healthcare_km	university_km	workplaces_km	shopping_centers_km	office_km	additional_education_km	preschool_km	big_church_km	church_synagogue_km	mosque_km	theater_km	museum_km	exhibition_km	...	cafe_count_1000_price_4000	cafe_count_1000_price_high	big_church_count_1000	church_count_1000	leisure_count_1000	sport_count_1000	market_count_1000	green_part_1500	prom_part_1500	office_count_1500	office_sqm_1500	trc_count_1500	trc_sqm_1500	cafe_count_1500	cafe_sum_1500_min_price_avg	cafe_sum_1500_max_price_avg	cafe_avg_price_1500	cafe_count_1500_na_price	cafe_count_1500_price_500	cafe_count_1500_price_1000	cafe_count_1500_price_1500	cafe_count_1500_price_2500	cafe_count_1500_price_4000	cafe_count_1500_price_high	big_church_count_1500	church_count_1500	leisure_count_1500	sport_count_1500	market_count_1500	green_part_2000	prom_part_2000	office_count_2000	office_sqm_2000	trc_count_2000	trc_sqm_2000	cafe_count_2000	cafe_sum_2000_min_price_avg	cafe_sum_2000_max_price_avg	cafe_avg_price_2000	cafe_count_2000_na_price	cafe_count_2000_price_500	cafe_count_2000_price_1000	cafe_count_2000_price_1500	cafe_count_2000_price_2500	cafe_count_2000_price_4000	cafe_count_2000_price_high	big_church_count_2000	church_count_2000	leisure_count_2000	sport_count_2000	market_count_2000	green_part_3000	prom_part_3000	office_count_3000	office_sqm_3000	trc_count_3000	trc_sqm_3000	cafe_count_3000	cafe_sum_3000_min_price_avg	cafe_sum_3000_max_price_avg	cafe_avg_price_3000	cafe_count_3000_na_price	cafe_count_3000_price_500	cafe_count_3000_price_1000	cafe_count_3000_price_1500	cafe_count_3000_price_2500	cafe_count_3000_price_4000	cafe_count_3000_price_high	big_church_count_3000	church_count_3000	mosque_count_3000	leisure_count_3000	sport_count_3000	market_count_3000	green_part_5000	prom_part_5000	office_count_5000	office_sqm_5000	trc_count_5000	trc_sqm_5000	cafe_count_5000	cafe_sum_5000_min_price_avg	cafe_sum_5000_max_price_avg	cafe_avg_price_5000	cafe_count_5000_na_price	cafe_count_5000_price_500	cafe_count_5000_price_1000	cafe_count_5000_price_1500	cafe_count_5000_price_2500	cafe_count_5000_price_4000	cafe_count_5000_price_high	big_church_count_5000	church_count_5000	mosque_count_5000	leisure_count_5000	sport_count_5000	market_count_5000	month	day
0	38135	-0.720188	-0.425838	-0.877825	-0.581216	0.531818	0.187170	1.295149	-0.222979	0.146649	0.362943	1.579349	-0.496943	-0.722049	2.249692	4.116125	1.104928	2.321928	4.294872	1.307487	-3.093263	-0.148247	-1.300880	0.326089	0.154780	-0.375633	0.265746	-0.539118	-0.682446	-0.124467	-0.121389	-0.127027	2.289409	2.386136	2.181858	1.758536	1.635214	1.873301	0.456361	0.326359	0.512294	2.249692	2.358689	2.129094	2.321928	2.413747	2.216802	2.281233	2.374503	2.177963	-0.161461	-0.166827	-0.153458	2.289390	2.396009	2.170917	3.698658	2.062212	0.705857	-1.693016	0.391978	2.414051	2.940625	-5.415450	0.782686	-1.051917	3.698107	-1.181503	1.074286	1.343795	0.275823	3.635613	-0.466868	-0.553255	-0.479885	-0.435708	-0.435708	-0.367592	-0.192589	-0.233743	-0.758421	-0.065234	-1.636107	-0.864453	-0.146032	0.171800	0.171800	-0.018409	0.155498	0.153153	-0.191873	-0.215017	-0.215017	-0.937407	-0.032554	1.051621	1.082121	1.102163	0.678459	1.866258	-1.030354	1.273101	0.566921	-0.611995	1.068773	-1.078169	-0.256843	0.253219	0.425039	0.738345	0.345405	0.131329	0.287966	0.808903	0.242382	0.413551	-0.467709	-0.054884	1.073371	1.493166	0.745606	0.104272	0.962672	-0.378084	1.490326	0.654183	0.257094	0.930275	-0.363237	-0.197951	-0.473754	-0.207520	-1.046394	1.402244	1.142573	1.296132	...	0.373905	0.224449	-1.516908	-0.951125	0.457361	-0.698101	0.614724	-0.007653	-0.923969	1.296533	1.314706	1.740068	1.765686	-0.469029	0.909483	0.538555	0.684330	1.060684	1.504421	2.105188	-0.710710	1.082391	0.537703	0.272655	-1.001678	-0.491847	0.633197	-0.762432	0.887953	-0.430031	-0.983962	1.533960	1.540772	2.092902	2.303388	-0.397717	0.753228	0.420868	0.550597	1.332940	1.994457	2.341679	-0.532825	1.337040	0.749555	0.316944	-0.807555	-0.390294	0.773571	-0.735301	1.110466	-0.637304	-1.043258	1.871263	1.824614	-0.875123	-0.841788	-0.379226	0.179678	-0.085962	0.017290	-0.738505	-0.546056	-0.502142	-0.415542	1.746133	1.096697	0.414079	-0.661712	-0.391445	-2.416831	1.030214	-0.831028	1.485452	-0.145386	-1.056496	-0.562970	-0.677949	-1.002645	-0.983062	-0.518350	-0.650138	-0.893287	-0.799249	-0.650806	-0.545564	-0.591706	-0.488643	3.044759	2.041240	0.548158	-0.566298	-0.475511	-1.587810	1.440184	-0.936058	-1.271634	-0.071315	1.644608
1	30475	1.242129	-0.083525	-0.213539	0.339382	0.531818	0.255150	-0.694237	-0.241597	1.313446	0.335827	-1.483901	1.515007	-0.945628	-1.338624	-0.130403	1.623708	-1.348669	-0.250098	1.552875	0.323283	-0.148247	1.124855	0.326089	1.832181	1.685920	0.265746	-0.931349	1.550957	-0.437313	-0.438222	-0.436383	-1.349119	-1.335979	-1.360330	-1.460973	-1.468277	-1.445139	-1.498586	-1.460280	-1.507933	-1.338624	-1.322969	-1.352238	-1.348669	-1.338617	-1.354803	-1.356053	-1.344436	-1.365700	-0.438010	-0.438158	-0.438357	-1.349680	-1.336207	-1.360952	-0.319273	-0.340464	-0.924861	0.530194	-0.454359	-0.449821	-0.351987	0.189085	0.782686	0.467651	-0.318451	0.793199	-0.767822	-0.274819	-0.338500	-0.365554	-0.894191	-0.072462	-0.029076	-0.012167	-0.012167	0.030438	-0.057449	0.367951	4.013384	-0.325963	0.500734	0.019043	0.958208	0.338201	0.338201	-0.500349	0.315782	0.293118	-0.599768	0.215116	0.215116	0.398599	0.052287	0.732605	0.722609	0.734415	0.413785	0.266975	-0.339099	0.592906	-0.054341	0.285373	1.056135	-0.559623	0.833124	-0.015204	1.062965	0.848426	0.665156	0.293251	0.466983	-0.202339	0.195739	1.188617	0.018733	0.054386	-0.326146	0.579614	0.609975	1.172162	1.081739	0.100419	0.813163	0.954658	0.051790	0.548483	-0.044727	-0.059619	-0.191324	0.100051	0.814213	0.460533	0.431872	-0.144518	...	0.373905	0.224449	0.700431	1.111609	0.457361	1.577157	0.614724	2.436839	-0.682165	1.296533	1.314706	1.740068	1.765686	-0.464624	0.295729	0.538555	0.444234	1.060684	1.504421	-0.561746	1.530591	-0.987468	0.537703	0.272655	1.054297	-0.533294	0.633197	-1.050444	0.887953	2.010009	-0.776539	1.533960	1.540772	-0.928094	-0.704428	-0.384394	-0.069406	-0.041665	-0.052826	1.332940	-0.588202	-0.517802	-0.542439	-0.807938	0.749555	0.316944	-0.807555	-0.390294	0.773571	-0.988001	1.110466	1.182109	-0.992953	1.871263	1.824614	-0.933155	-0.909670	-0.384456	-0.345776	-0.175224	-0.241776	1.587716	-0.552448	-0.471351	-0.454169	-0.649666	1.096697	0.414079	-0.661712	-0.328256	0.413765	1.030214	-0.831028	1.485452	1.453722	-0.560321	-0.558772	-0.638758	-1.071682	-1.040531	-0.516717	-0.247162	-0.161538	-0.195068	-0.650806	-0.551490	-0.567129	-0.515133	-0.504662	-0.592727	0.548158	-0.543422	-0.457269	0.629798	-0.822869	-0.971107	-1.271634	-0.071315	1.644608
2	30476	-0.646967	-0.270562	-0.767110	-1.041515	-2.399684	0.064159	-0.818574	-0.232642	-1.020148	-0.347291	0.894814	-0.903506	0.492785	0.253439	-0.528036	0.067370	0.230682	0.003536	-0.164841	0.323283	0.544834	-0.330586	0.326089	0.154780	1.685920	0.265746	-0.800605	-0.633540	-0.369586	-0.374809	-0.365058	0.242451	0.213153	0.273095	0.957094	0.861570	1.048313	1.048518	0.885588	1.116125	0.253439	0.220367	0.288118	0.230682	0.210944	0.250743	0.248909	0.221422	0.277602	-0.363491	-0.375945	-0.351286	0.243925	0.216166	0.272868	0.451845	0.907438	1.206374	0.530194	0.439288	-0.554917	0.590147	0.189085	-1.335251	0.467651	0.452960	0.793199	-0.749215	1.332822	-0.236113	-0.318665	-0.484673	-0.500450	-0.415593	-0.375304	-0.375304	-0.372301	-0.320054	-0.114906	0.495733	-0.236976	-0.108440	0.710064	-0.197488	-0.133154	-0.133154	0.913342	-0.074933	-0.062160	0.351986	-0.213475	-0.213475	1.815171	-0.548335	-0.620620	-0.623805	-0.660199	-0.573558	-0.984264	-0.527623	0.114717	1.696487	0.226541	-0.607469	1.485754	-0.321478	-1.357320	-1.526046	0.507795	-0.616638	-0.214814	-0.571856	-0.273547	0.340189	-0.888731	-0.136464	-0.503585	-0.574268	-0.995849	-0.548705	-0.686932	-0.697363	-0.577568	-0.261485	-0.104027	-0.045401	-0.288713	0.126843	-0.328428	-0.274565	0.114893	0.399420	-0.897984	-0.809948	-0.787819	...	0.373905	0.224449	0.700431	1.111609	-2.349895	-0.271490	-1.163312	0.668735	-0.501730	-0.851352	-0.880011	-0.885156	1.765686	-0.394142	-0.358901	-0.379051	-0.372144	-1.021822	-0.675329	-0.497867	-0.682517	-0.967941	0.537703	0.272655	-1.001678	-0.533294	-1.672948	0.005601	-0.586660	1.411631	-0.311998	-0.697959	-0.780531	-0.745004	-0.693237	-0.314451	-0.488785	-0.458505	-0.471186	-0.828571	-0.533252	-0.423533	-0.503980	-0.795467	0.749555	0.316944	-0.775026	-0.390294	-1.243136	-0.166727	-0.183291	1.648513	-0.349047	-0.602747	-0.672130	-0.701027	-0.886902	-0.313844	-0.715637	-0.602578	-0.647572	-0.738505	-0.507702	-0.371281	-0.415542	-0.640344	1.096697	0.414079	-0.636512	-0.391445	0.413765	-0.942915	-0.294473	-0.298954	0.223358	0.410600	-0.453828	-0.568485	-0.381312	-0.368899	-0.257125	-0.703292	-0.694517	-0.698552	-0.600535	-0.261104	-0.223047	-0.263478	-0.409503	-0.537852	0.548158	-0.360410	-0.274848	0.629798	-0.627028	0.062841	0.314596	-0.071315	1.644608
3	30477	0.441582	0.114099	0.782889	0.339382	0.531818	0.245439	-0.818574	-0.095953	0.146649	0.158707	-1.429040	0.202462	-0.876407	-1.282999	-0.130403	1.623708	-1.297228	-0.250098	1.552875	0.323283	-0.148247	1.124855	0.326089	1.832181	-0.719225	0.265746	1.945014	1.550957	-0.467691	-0.469002	-0.466432	-1.295325	-1.281853	-1.306751	-1.406720	-1.411452	-1.393801	-1.446508	-1.412660	-1.454015	-1.282999	-1.267095	-1.296988	-1.297228	-1.286981	-1.303228	-1.301647	-1.289777	-1.311887	-0.466179	-0.467595	-0.465676	-1.295813	-1.282128	-1.307431	-0.319273	-0.340464	-0.924861	0.530194	-0.454359	-0.449821	-0.351987	0.189085	0.782686	0.467651	-0.318451	0.793199	-0.767822	-0.274819	-0.338500	-0.365554	-0.466868	0.526366	0.402678	0.342570	0.342570	0.744120	0.451979	0.678120	-0.845483	-0.481173	-1.056021	0.294800	0.419513	1.581426	1.581426	-0.018409	1.675431	1.885978	-0.191873	1.584831	1.584831	-0.974055	0.425569	1.342953	1.345771	1.347650	0.867355	0.725303	1.231935	2.792327	-1.466299	1.319367	2.017605	-0.559623	0.456431	-0.015204	0.825626	1.216489	0.812743	0.549925	1.013913	1.133013	-0.324912	2.006252	1.435504	0.884850	1.716821	1.252237	1.274876	1.097910	1.396909	0.533293	1.230477	1.235153	1.576179	1.977498	-0.393120	0.461845	0.952853	-0.828824	-0.282956	1.153889	1.609682	1.650187	...	0.373905	0.224449	0.700431	-0.890456	0.457361	1.577157	0.614724	0.534457	-0.772382	1.296533	1.314706	1.740068	1.765686	-0.473434	5.205693	5.257657	5.246327	1.060684	-0.740591	2.105188	1.530591	1.082391	0.537703	0.272655	1.054297	-0.450399	0.633197	1.829679	0.887953	0.728076	-0.853602	1.533960	1.540772	2.092902	2.303388	-0.404378	4.705062	4.737761	4.730574	1.332940	-0.588202	2.341679	2.130475	1.337040	0.749555	0.316944	1.339376	-0.354867	0.773571	2.297097	1.110466	0.842573	-1.214296	1.871263	1.824614	2.606797	2.593685	-0.407993	5.606113	5.716608	5.684703	1.587716	-0.552448	2.884863	2.975891	1.746133	1.096697	0.414079	1.782651	-0.359850	0.413765	1.030214	2.495614	1.485452	0.098314	-1.555968	2.690303	2.739924	2.759871	2.646635	-0.541207	5.611585	5.493050	5.543491	2.214667	-0.569269	3.328373	-0.535001	-0.504662	-0.574435	0.548158	2.842292	-0.475511	0.629798	1.440184	-1.146353	1.900826	-0.071315	1.644608
4	30478	-0.671374	0.255259	0.782889	0.339382	0.531818	0.255150	1.295149	-0.241597	1.313446	0.335827	-1.483901	1.515007	-0.945628	-1.338624	-0.130403	1.623708	-1.348669	-0.250098	1.552875	0.323283	-0.148247	1.124855	0.326089	1.832181	1.685920	0.265746	-0.931349	1.550957	-0.437313	-0.438222	-0.436383	-1.349119	-1.335979	-1.360330	-1.460973	-1.468277	-1.445139	-1.498586	-1.460280	-1.507933	-1.338624	-1.322969	-1.352238	-1.348669	-1.338617	-1.354803	-1.356053	-1.344436	-1.365700	-0.438010	-0.438158	-0.438357	-1.349680	-1.336207	-1.360952	-0.319273	-0.340464	-0.924861	0.530194	-0.454359	-0.449821	-0.351987	0.189085	0.782686	0.467651	-0.318451	0.793199	-0.767822	-0.274819	-0.338500	-0.365554	-0.894191	-0.408629	-0.315732	-0.281484	-0.281484	-0.074746	-0.080142	0.410462	0.125178	-0.544857	0.604902	0.022200	1.048473	-0.105494	-0.105494	-0.500349	-0.138801	-0.203974	-0.599768	0.096659	0.096659	-0.583196	0.101687	0.753227	0.743037	0.754332	0.429647	0.793468	-0.339099	0.213926	-0.054341	-0.012711	0.890779	-0.559623	0.891355	-0.015204	1.113917	0.889262	0.746749	0.407849	0.477322	-0.187772	0.249785	0.712270	0.111053	-0.275717	-0.653102	0.639246	0.613893	1.258119	0.929203	-0.280762	0.877478	0.921405	0.065145	0.737480	-0.140728	-0.082848	-0.338075	0.123798	0.888668	0.522004	0.469048	-0.102337	...	0.373905	0.224449	0.700431	1.111609	0.457361	1.577157	0.614724	1.473157	-0.879385	1.296533	1.314706	1.740068	1.765686	-0.464624	0.295729	0.538555	0.444234	1.060684	1.504421	-0.561746	1.530591	-0.987468	0.537703	0.272655	-1.001678	-0.533294	0.633197	1.829679	0.887953	1.247721	-0.844239	1.533960	1.540772	-0.836549	-0.683500	-0.384394	-0.069406	-0.041665	-0.052826	1.332940	-0.588202	-0.517802	-0.542439	-0.807938	0.749555	0.316944	-0.807555	-0.390294	0.773571	-0.861651	1.110466	1.339044	-1.128218	1.871263	1.824614	-0.933155	-0.909670	-0.387071	-0.242564	-0.102492	-0.157053	1.587716	-0.552448	-0.479049	-0.454169	-0.649666	1.096697	0.414079	-0.661712	-0.359850	0.413765	1.030214	-0.866798	1.485452	1.136091	-0.682304	-0.562970	-0.655584	-1.140719	-1.065936	-0.516717	-0.147658	-0.102007	-0.119959	-0.667563	-0.551490	-0.567129	-0.508511	-0.504662	-0.592727	0.548158	-0.543422	-0.439027	0.629798	-0.822869	-0.988632	-1.271634	-0.071315	1.644608

	id	price_doc
0	30474	0.383503
1	30475	1.256324
2	30476	0.514588
3	30477	0.596932
4	30478	0.491853

	Datatype	NAs	Uniques	levels
id	int64	0	30471	[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...
timestamp	object	0	1161	[2011-08-20, 2011-08-23, 2011-08-27, 2011-09-0...
full_sq	int64	0	211	[43, 34, 89, 77, 67, 25, 44, 42, 36, 38, 31, 5...
life_sq	float64	6383	175	[27.0, 19.0, 29.0, 50.0, 77.0, 46.0, 14.0, 44....
floor	float64	167	41	[4.0, 3.0, 2.0, 9.0, 14.0, 10.0, 5.0, 12.0, 11...
max_floor	float64	9572	49	[nan, 17.0, 5.0, 22.0, 16.0, 9.0, 8.0, 0.0, 24...
material	float64	9572	6	[nan, 1.0, 2.0, 4.0, 6.0, 5.0, 3.0]
build_year	float64	13605	119	[nan, 1907.0, 1980.0, 2014.0, 1970.0, 1982.0, ...
num_room	float64	9572	13	[nan, 2.0, 1.0, 3.0, 4.0, 5.0, 6.0, 0.0, 19.0,...
kitch_sq	float64	9572	74	[nan, 11.0, 12.0, 0.0, 8.0, 1.0, 6.0, 10.0, 9....
state	float64	13559	5	[nan, 3.0, 1.0, 2.0, 4.0, 33.0]
product_type	object	0	2	[Investment, OwnerOccupier]
sub_area	object	0	146	[Bibirevo, Nagatinskij Zaton, Tekstil'shhiki, ...
area_m	float64	0	146	[6407578.1, 9589336.912, 4808269.831, 12583535...
raion_popul	int64	0	146	[155572, 115352, 101708, 178473, 108171, 43795...
green_zone_part	float64	0	146	[0.189727117, 0.37260204399999997, 0.112559644...
indust_part	float64	0	132	[6.99893e-05, 0.049637257000000004, 0.11853738...
children_preschool	int64	0	146	[9576, 6880, 5879, 13087, 5706, 2418, 2459, 65...
preschool_quota	float64	6688	121	[5001.0, 3119.0, 1463.0, 6839.0, 3240.0, 852.0...
preschool_education_centers_raion	int64	0	13	[5, 4, 9, 7, 2, 3, 13, 8, 6, 1, 10, 11, 0]
children_school	int64	0	146	[10309, 7759, 6207, 13670, 6748, 2514, 2810, 6...
school_quota	float64	6685	125	[11065.0, 6237.0, 5580.0, 17063.0, 7770.0, 201...
school_education_centers_raion	int64	0	14	[5, 8, 7, 10, 9, 3, 6, 4, 14, 1, 13, 11, 2, 0]
school_education_centers_top_20_raion	int64	0	3	[0, 1, 2]
hospital_beds_raion	float64	14441	79	[240.0, 229.0, 1183.0, nan, 562.0, 4849.0, 189...
healthcare_centers_raion	int64	0	7	[1, 4, 0, 3, 2, 5, 6]
university_top_20_raion	int64	0	4	[0, 2, 1, 3]
sport_objects_raion	int64	0	24	[7, 6, 5, 17, 25, 4, 3, 29, 12, 16, 2, 0, 10, ...
additional_education_raion	int64	0	12	[3, 1, 6, 2, 0, 16, 8, 4, 5, 11, 10, 7]
culture_objects_top_25	object	0	2	[no, yes]
culture_objects_top_25_raion	int64	0	6	[0, 1, 3, 2, 4, 10]
shopping_centers_raion	int64	0	16	[16, 3, 0, 11, 10, 6, 5, 7, 15, 2, 1, 9, 4, 8,...
office_raion	int64	0	30	[1, 0, 4, 93, 19, 9, 7, 3, 84, 14, 2, 6, 5, 48...
thermal_power_plant_raion	object	0	2	[no, yes]
incineration_raion	object	0	2	[no, yes]
oil_chemistry_raion	object	0	2	[no, yes]
radiation_raion	object	0	2	[no, yes]
railroad_terminal_raion	object	0	2	[no, yes]
big_market_raion	object	0	2	[no, yes]
nuclear_reactor_raion	object	0	2	[no, yes]

**SberBank Russian Housing Market***¶

Data Pre-Processing¶

* In data Pre-Processing we will not change the data just see what the data is all about,¶

*how many Numerical, Categorical Variables are Present¶

* Percentage of Missing values and plotting the median values for NA values and given records of that column¶

* Plotting the numerical and Categorical columns w.r.t Target Variable¶

1. Seeing the Percentage Of Missing Values¶

2. Plotting w.r.t Columns Having NA Values VS Present Records¶

3.NUMERICAL VARIABLES¶

* We are having different types of numerical variables¶

a.Numerical Continous Variables¶

b.Numerical Discrete Variables¶

c.Date variables¶

C. Date variables Present or not¶

B. Numerical Discrete Variables¶

A. Numerical Continous Variables¶

Applying Log¶

Finding Outliers¶

CATEGORICAL VARIABLES¶

FEATURE ENGINEERING¶

Handling Categorical NA values¶

Handling Numerical NA values¶

RARE VARIABLE¶

TARGET ENCODING¶

Performing log normal distribution on numerical continous variables¶

FEATURE SCALING¶

FEATURE SELECTION¶

SberBank Russian Housing Market*¶